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Sun, Guoxiang, Xiaochan Wang, Ye Sun, Yongqian Ding, and Wei Lu. "Measurement Method Based on Multispectral Three-Dimensional Imaging for the Chlorophyll Contents of Greenhouse Tomato Plants." Sensors 19, no. 15 (July 30, 2019): 3345. http://dx.doi.org/10.3390/s19153345.
Повний текст джерелаАнотація:
Nondestructive plant growth measurement is essential for researching plant growth and health. A nondestructive measurement system to retrieve plant information includes the measurement of morphological and physiological information, but most systems use two independent measurement systems for the two types of characteristics. In this study, a highly integrated, multispectral, three-dimensional (3D) nondestructive measurement system for greenhouse tomato plants was designed. The system used a Kinect sensor, an SOC710 hyperspectral imager, an electric rotary table, and other components. A heterogeneous sensing image registration technique based on the Fourier transform was proposed, which was used to register the SOC710 multispectral reflectance in the Kinect depth image coordinate system. Furthermore, a 3D multiview RGB-D image-reconstruction method based on the pose estimation and self-calibration of the Kinect sensor was developed to reconstruct a multispectral 3D point cloud model of the tomato plant. An experiment was conducted to measure plant canopy chlorophyll and the relative chlorophyll content was measured by the soil and plant analyzer development (SPAD) measurement model based on a 3D multispectral point cloud model and a single-view point cloud model and its performance was compared and analyzed. The results revealed that the measurement model established by using the characteristic variables from the multiview point cloud model was superior to the one established using the variables from the single-view point cloud model. Therefore, the multispectral 3D reconstruction approach is able to reconstruct the plant multispectral 3D point cloud model, which optimizes the traditional two-dimensional image-based SPAD measurement method and can obtain a precise and efficient high-throughput measurement of plant chlorophyll.
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Sun, Guoxiang, Yongqian Ding, Xiaochan Wang, Wei Lu, Ye Sun, and Hongfeng Yu. "Nondestructive Determination of Nitrogen, Phosphorus and Potassium Contents in Greenhouse Tomato Plants Based on Multispectral Three-Dimensional Imaging." Sensors 19, no. 23 (December 1, 2019): 5295. http://dx.doi.org/10.3390/s19235295.
Повний текст джерелаАнотація:
Measurement of plant nitrogen (N), phosphorus (P), and potassium (K) levels are important for determining precise fertilization management approaches for crops cultivated in greenhouses. To accurately, rapidly, stably, and nondestructively measure the NPK levels in tomato plants, a nondestructive determination method based on multispectral three-dimensional (3D) imaging was proposed. Multiview RGB-D images and multispectral images were synchronously collected, and the plant multispectral reflectance was registered to the depth coordinates according to Fourier transform principles. Based on the Kinect sensor pose estimation and self-calibration, the unified transformation of the multiview point cloud coordinate system was realized. Finally, the iterative closest point (ICP) algorithm was used for the precise registration of multiview point clouds and the reconstruction of plant multispectral 3D point cloud models. Using the normalized grayscale similarity coefficient, the degree of spectral overlap, and the Hausdorff distance set, the accuracy of the reconstructed multispectral 3D point clouds was quantitatively evaluated, the average value was 0.9116, 0.9343 and 0.41 cm, respectively. The results indicated that the multispectral reflectance could be registered to the Kinect depth coordinates accurately based on the Fourier transform principles, the reconstruction accuracy of the multispectral 3D point cloud model met the model reconstruction needs of tomato plants. Using back-propagation artificial neural network (BPANN), support vector machine regression (SVMR), and gaussian process regression (GPR) methods, determination models for the NPK contents in tomato plants based on the reflectance characteristics of plant multispectral 3D point cloud models were separately constructed. The relative error (RE) of the N content by BPANN, SVMR and GPR prediction models were 2.27%, 7.46% and 4.03%, respectively. The RE of the P content by BPANN, SVMR and GPR prediction models were 3.32%, 8.92% and 8.41%, respectively. The RE of the K content by BPANN, SVMR and GPR prediction models were 3.27%, 5.73% and 3.32%, respectively. These models provided highly efficient and accurate measurements of the NPK contents in tomato plants. The NPK contents determination performance of these models were more stable than those of single-view models.
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Wisotzky, Eric L., Jean-Claude Rosenthal, Anna Hilsmann, Peter Eisert, and Florian C. Uecker. "A multispectral 3D-Endoscope for Cholesteatoma Removal." Current Directions in Biomedical Engineering 6, no. 3 (September 1, 2020): 257–60. http://dx.doi.org/10.1515/cdbme-2020-3065.
Повний текст джерелаАнотація:
AbstractWe present a stereo-multispectral endoscopic prototype using a filter-wheel to guide the removal of cholesteatoma tissue in the middle ear. An image-based method is used that combines multispectral tissue classification for the detection of tissue to be removed and 3Dreconstruction to determine its metric dimensions. The multispectral illumination used for tissue classification ranges from λ = 400 nm to λ = 500 nm with step-size of 20 nm, which results in six different narrow-band illumination modes. For classical RGB imaging and metric calculations, a broadband illumination mode is applied before and after the narrow-band illumination. The spectral information is augmented into the broadband mode using an overlay technique. The combination of multispectral imaging with stereoscopic 3D-reconstruction results in new valuable visualization of intraoperative data. This allows to generate a 3D-model of the patients anatomy highlighting the identified malicious tissue and compare the anatomical dimensions with pre-operative CT data.
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Zainuddin, K., Z. Majid, M. F. M. Ariff, K. M. Idris, M. A. Abbas, and N. Darwin. "3D MODELING FOR ROCK ART DOCUMENTATION USING LIGHTWEIGHT MULTISPECTRAL CAMERA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W9 (January 31, 2019): 787–93. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w9-787-2019.
Повний текст джерелаАнотація:
<p><strong>Abstract.</strong> This paper discusses the use of the lightweight multispectral camera to acquire three-dimensional data for rock art documentation application. The camera consists of five discrete bands, used for taking the motifs of the rock art paintings on a big structure of a cave based on the close-range photogrammetry technique. The captured images then processed using commercial structure-from-motion photogrammetry software, which automatically extracts the tie point. The extracted tie points were then used as input to generate a dense point cloud based on the multi-view stereo (MVS) and produced the multispectral 3D model, and orthophotos in a different wavelength. For comparison, the paintings and the wall surface also observed by using terrestrial laser scanner which capable of recording thousands of points in a short period of time with high accuracy. The cloud-to-cloud comparison between multispectral and TLS 3D point cloud show a sub-cm discrepancy, considering the used of the natural features as control target during 3D construction. Nevertheless, the processing also provides photorealistic orthophoto, indicates the advantages of the multispectral camera in generating dense 3D point cloud as TLS, photorealistic 3D model as RGB optic camera, and also with the multiwavelength output.</p>
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LARSON, ADAM M., ANTHONY LEE, PO-FENG LEE, KAYLA J. BAYLESS, and ALVIN T. YEH. "ULTRASHORT PULSE MULTISPECTRAL NON-LINEAR OPTICAL MICROSCOPY." Journal of Innovative Optical Health Sciences 02, no. 01 (January 2009): 27–35. http://dx.doi.org/10.1142/s1793545809000292.
Повний текст джерелаАнотація:
Ultrashort pulse, multispectral non-linear optical microscopy (NLOM) is developed and used to image, simultaneously, a mixed population of cells expressing different fluorescent protein mutants in a 3D tissue model of angiogenesis. Broadband, sub-10-fs pulses are used to excite multiple fluorescent proteins and generate second harmonic in collagen. A 16-channel multispectral detector is used to delineate the multiple non-linear optical signals, pixel by pixel, in NLOM. The ability to image multiple fluorescent protein mutants and collagen, enables serial measurements of cell-cell and cell-matrix interactions in our 3D tissue model and characterization of fundamental processes in angiogenic morphogenesis.
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Es Sebar, Leila, Luca Lombardo, Marco Parvis, Emma Angelini, Alessandro Re, and Sabrina Grassini. "A metrological approach for multispectral photogrammetry." ACTA IMEKO 10, no. 4 (December 30, 2021): 111. http://dx.doi.org/10.21014/acta_imeko.v10i4.1194.
Повний текст джерелаАнотація:
<p>This paper presents the design and development of a three-dimensional reference object for the metrological quality assessment of photogrammetry-based techniques, for application in the cultural heritage field. The reference object was 3D printed, with nominal manufacturing uncertainty of the order of 0.01 mm. The object was realized as a dodecahedron, and in each face, a different pictorial preparation was inserted. The preparations include several pigments, binders, and varnishes, to be representative of the materials and techniques used historically by artists.</p><p>Since the reference object’s shape, size and uncertainty are known, it is possible to use this object as a reference to evaluate the quality of a 3D model from the metric point of view. In particular, verification of dimensional precision and accuracy are performed using the standard deviation on measurements acquired on the reference object and the final 3D model. In addition, the object can be used as a reference for UV-induced Visible Luminescence (UVL) acquisition, being the materials employed UV-fluorescent. Results obtained with visible-reflected and UVL images are presented and discussed.</p>
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Lerma, J. L., M. Cabrelles, T. S. Akasheh, and N. A. Haddad. "Documentation of Weathered Architectural Heritage with Visible, near Infrared, Thermal and Laser Scanning Data." International Journal of Heritage in the Digital Era 1, no. 2 (June 2012): 251–75. http://dx.doi.org/10.1260/2047-4970.1.2.251.
Повний текст джерелаАнотація:
Documentation of cultural heritage requires simple, quick and easy to use multi-sensor approaches to determine the state of conservation of monuments and sites. The documentation of a highly weathered architectural heritage such as the Obelisk Tomb is a good example to test the performance integrating multispectral imagery and laser scanning data. The Obelisk Tomb is the first important façade that a visitor sees while entering to the archaeological site of Petra in Jordan. The rich architectural formations carry Egyptian, Hellenistic and Nabataean influences. The damage that was inflicted on this unique monument led us to study it applying a number of modern digital techniques including 3D scanning, multispectral photography with visible and near infrared images, and thermography. All the multiband content is initially registered onto different multispectral bands. The multispectral information is enhanced and eventually draped onto the 3D laser scanning model in order to improve documentation and analysis of the state of conservation. Our results integrating the multispectral data, thermography and terrestrial laser scanning clearly enhance the power of diagnosis over the Obelisk Tomb with state-of-the-art optical equipment and image processing software. Furthermore, the capacity to examine, analyse and detect the existing damages is enhanced by the false colour processing of the input photographic data. Weathering effects are highlighted onto the 3D model and shed some light on the causes of the damages.
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Luo, Heng, Biao He, Renzhong Guo, Weixi Wang, Xi Kuai, Bilu Xia, Yuan Wan, Ding Ma, and Linfu Xie. "Urban Building Extraction and Modeling Using GF-7 DLC and MUX Images." Remote Sensing 13, no. 17 (August 27, 2021): 3414. http://dx.doi.org/10.3390/rs13173414.
Повний текст джерелаАнотація:
Urban modeling and visualization are highly useful in the development of smart cities. Buildings are the most prominent features in the urban environment, and are necessary for urban decision support; thus, buildings should be modeled effectively and efficiently in three dimensions (3D). In this study, with the help of Gaofen-7 (GF-7) high-resolution stereo mapping satellite double-line camera (DLC) images and multispectral (MUX) images, the boundary of a building is segmented via a multilevel features fusion network (MFFN). A digital surface model (DSM) is generated to obtain the elevation of buildings. The building vector with height information is processed using a 3D modeling tool to create a white building model. The building model, DSM, and multispectral fused image are then imported into the Unreal Engine 4 (UE4) to complete the urban scene level, vividly rendered with environmental effects for urban visualization. The results of this study show that high accuracy of 95.29% is achieved in building extraction using our proposed method. Based on the extracted building vector and elevation information from the DSM, building 3D models can be efficiently created in Level of Details 1 (LOD1). Finally, the urban scene is produced for realistic 3D visualization. This study shows that high-resolution stereo mapping satellite images are useful in 3D modeling for urban buildings and can support the generation and visualization of urban scenes in a large area for different applications.
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Wisotzky, Eric L., Jean-Claude Rosenthal, Ulla Wege, Anna Hilsmann, Peter Eisert, and Florian C. Uecker. "Surgical Guidance for Removal of Cholesteatoma Using a Multispectral 3D-Endoscope." Sensors 20, no. 18 (September 17, 2020): 5334. http://dx.doi.org/10.3390/s20185334.
Повний текст джерелаАнотація:
We develop a stereo-multispectral endoscopic prototype in which a filter-wheel is used for surgical guidance to remove cholesteatoma tissue in the middle ear. Cholesteatoma is a destructive proliferating tissue. The only treatment for this disease is surgery. Removal is a very demanding task, even for experienced surgeons. It is very difficult to distinguish between bone and cholesteatoma. In addition, it can even reoccur if not all tissue particles of the cholesteatoma are removed, which leads to undesirable follow-up operations. Therefore, we propose an image-based method that combines multispectral tissue classification and 3D reconstruction to identify all parts of the removed tissue and determine their metric dimensions intraoperatively. The designed multispectral filter-wheel 3D-endoscope prototype can switch between narrow-band spectral and broad-band white illumination, which is technically evaluated in terms of optical system properties. Further, it is tested and evaluated on three patients. The wavelengths 400 nm and 420 nm are identified as most suitable for the differentiation task. The stereoscopic image acquisition allows accurate 3D surface reconstruction of the enhanced image information. The first results are promising, as the cholesteatoma can be easily highlighted, correctly identified, and visualized as a true-to-scale 3D model showing the patient-specific anatomy.
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Suo, McGovern, and Gilmer. "Coastal Dune Vegetation Mapping Using a Multispectral Sensor Mounted on an UAS." Remote Sensing 11, no. 15 (August 2, 2019): 1814. http://dx.doi.org/10.3390/rs11151814.
Повний текст джерелаАнотація:
Vegetation mapping, identifying the type and distribution of plant species, is important for analysing vegetation dynamics, quantifying spatial patterns of vegetation evolution, analysing the effects of environmental changes and predicting spatial patterns of species diversity. Such analysis can contribute to the development of targeted land management actions that maintain biodiversity and ecological functions. This paper presents a methodology for 3D vegetation mapping of a coastal dune complex using a multispectral camera mounted on an unmanned aerial system with particular reference to the Buckroney dune complex in Co. Wicklow, Ireland. Unmanned aerial systems (UAS), also known as unmanned aerial vehicles (UAV) or drones, have enabled high-resolution and high-accuracy ground-based data to be gathered quickly and easily on-site. The Sequoia multispectral sensor used in this study has green, red, red edge and near-infrared wavebands, and a regular camer with red, green and blue wavebands (RGB camera), to capture both visible and near-infrared (NIR) imagery of the land surface. The workflow of 3D vegetation mapping of the study site included establishing coordinated ground control points, planning the flight mission and camera parameters, acquiring the imagery, processing the image data and performing features classification. The data processing outcomes included an orthomosaic model, a 3D surface model and multispectral imagery of the study site, in the Irish Transverse Mercator (ITM) coordinate system. The planimetric resolution of the RGB sensor-based outcomes was 0.024 m while multispectral sensor-based outcomes had a planimetric resolution of 0.096 m. High-resolution vegetation mapping was successfully generated from these data processing outcomes. There were 235 sample areas (1 m × 1 m) used for the accuracy assessment of the classification of the vegetation mapping. Feature classification was conducted using nine different classification strategies to examine the efficiency of multispectral sensor data for vegetation and contiguous land cover mapping. The nine classification strategies included combinations of spectral bands and vegetation indices. Results show classification accuracies, based on the nine different classification strategies, ranging from 52% to 75%.
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Zhang, C., A. Breitbarth, M. Rosenberger, and G. Notni. "3D model based shading correction for the enhancement of multispectral colour measurement accuracy." Journal of Physics: Conference Series 1065 (August 2018): 032008. http://dx.doi.org/10.1088/1742-6596/1065/3/032008.
Повний текст джерела
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Rincón, Manuel García, Diego Mendez, and Julian D. Colorado. "Four-Dimensional Plant Phenotyping Model Integrating Low-Density LiDAR Data and Multispectral Images." Remote Sensing 14, no. 2 (January 13, 2022): 356. http://dx.doi.org/10.3390/rs14020356.
Повний текст джерелаАнотація:
High-throughput platforms for plant phenotyping usually demand expensive high-density LiDAR devices with computational intense methods for characterizing several morphological variables. In fact, most platforms require offline processing to achieve a comprehensive plant architecture model. In this paper, we propose a low-cost plant phenotyping system based on the sensory fusion of low-density LiDAR data with multispectral imagery. Our contribution is twofold: (i) an integrated phenotyping platform with embedded processing methods capable of providing real-time morphological data, and (ii) a multi-sensor fusion algorithm that precisely match the 3D LiDAR point-cloud data with the corresponding multispectral information, aiming for the consolidation of four-dimensional plant models. We conducted extensive experimental tests over two plants with different morphological structures, demonstrating the potential of the proposed solution for enabling real-time plant architecture modeling in the field, based on low-density LiDARs.
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Matikainen, Leena, Juha Hyyppä, and Paula Litkey. "MULTISPECTRAL AIRBORNE LASER SCANNING FOR AUTOMATED MAP UPDATING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 323–30. http://dx.doi.org/10.5194/isprs-archives-xli-b3-323-2016.
Повний текст джерелаАнотація:
During the last 20 years, airborne laser scanning (ALS), often combined with multispectral information from aerial images, has shown its high feasibility for automated mapping processes. Recently, the first multispectral airborne laser scanners have been launched, and multispectral information is for the first time directly available for 3D ALS point clouds. This article discusses the potential of this new single-sensor technology in map updating, especially in automated object detection and change detection. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from a random forests analysis suggest that the multispectral intensity information is useful for land cover classification, also when considering ground surface objects and classes, such as roads. An out-of-bag estimate for classification error was about 3% for separating classes asphalt, gravel, rocky areas and low vegetation from each other. For buildings and trees, it was under 1%. According to feature importance analyses, multispectral features based on several channels were more useful that those based on one channel. Automatic change detection utilizing the new multispectral ALS data, an old digital surface model (DSM) and old building vectors was also demonstrated. Overall, our first analyses suggest that the new data are very promising for further increasing the automation level in mapping. The multispectral ALS technology is independent of external illumination conditions, and intensity images produced from the data do not include shadows. These are significant advantages when the development of automated classification and change detection procedures is considered.
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Matikainen, Leena, Juha Hyyppä, and Paula Litkey. "MULTISPECTRAL AIRBORNE LASER SCANNING FOR AUTOMATED MAP UPDATING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 323–30. http://dx.doi.org/10.5194/isprsarchives-xli-b3-323-2016.
Повний текст джерелаАнотація:
During the last 20 years, airborne laser scanning (ALS), often combined with multispectral information from aerial images, has shown its high feasibility for automated mapping processes. Recently, the first multispectral airborne laser scanners have been launched, and multispectral information is for the first time directly available for 3D ALS point clouds. This article discusses the potential of this new single-sensor technology in map updating, especially in automated object detection and change detection. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from a random forests analysis suggest that the multispectral intensity information is useful for land cover classification, also when considering ground surface objects and classes, such as roads. An out-of-bag estimate for classification error was about 3% for separating classes asphalt, gravel, rocky areas and low vegetation from each other. For buildings and trees, it was under 1%. According to feature importance analyses, multispectral features based on several channels were more useful that those based on one channel. Automatic change detection utilizing the new multispectral ALS data, an old digital surface model (DSM) and old building vectors was also demonstrated. Overall, our first analyses suggest that the new data are very promising for further increasing the automation level in mapping. The multispectral ALS technology is independent of external illumination conditions, and intensity images produced from the data do not include shadows. These are significant advantages when the development of automated classification and change detection procedures is considered.
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Wang, Xuewen, Qingzhan Zhao, Feng Han, Jianxin Zhang, and Ping Jiang. "Canopy Extraction and Height Estimation of Trees in a Shelter Forest Based on Fusion of an Airborne Multispectral Image and Photogrammetric Point Cloud." Journal of Sensors 2021 (June 27, 2021): 1–13. http://dx.doi.org/10.1155/2021/5519629.
Повний текст джерелаАнотація:
To reduce data acquisition cost, this study proposed a novel method of individual tree height estimation and canopy extraction based on fusion of an airborne multispectral image and photogrammetric point cloud. A fixed-wing drone was deployed to acquire the true color and multispectral images of a shelter forest. The Structure-from-Motion (SfM) algorithm was used to reconstruct the 3D point cloud of the canopy. The 3D point cloud was filtered to acquire the ground point cloud and then interpolated to a Digital Elevation Model (DEM) using the Radial Basis Function Neural Network (RBFNN). The DEM was subtracted from the Digital Surface Model (DSM) generated from the original point cloud to get the canopy height model (CHM). The CHM was processed for the crown extraction using local maximum filters and watershed segmentation. Then, object-oriented methods were employed in the combination of 12 bands and CHM for image segmentation. To extract the tree crown, the Support Vector Machine (SVM) algorithm was used. The result of the object-oriented method was vectorized and superimposed on the CHM to estimate the tree height. Experimental results demonstrated that it is efficient to employ point cloud and the proposed approach has great potential in the tree height estimation. The proposed object-oriented method based on fusion of a multispectral image and CHM effectively reduced the oversegmentation and undersegmentation, with an increase in the F -score by 0.12–0.17. Our findings provided a reference for the health and change monitoring of shelter forests as well.
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Poudel, A., and E. Bevilacqua. "ASSESSING RED PINE SEEDLINGS USING UAV POINT CLOUDS AND FIELD-VERIFIED DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-M-2-2022 (July 25, 2022): 173–76. http://dx.doi.org/10.5194/isprs-archives-xlvi-m-2-2022-173-2022.
Повний текст джерелаАнотація:
Abstract. Accurate, reliable, and cost-efficient approaches to forest monitoring are critical for sustainable forest management. The use of digital photogrammetry for tree height estimation is well-known among forest managers and remote sensing researchers. Satellite remote sensing has not been very successful in providing detailed and reliable estimates of tree height. Unmanned Aerial Vehicles (UAVs) are one of the latest remote sensing platforms to get forest attributes information at very high temporal and spatial resolution. This study assessed the potential of using digital aerial photogrammetry point clouds and UAV acquired high-resolution imagery to estimate red pine seedlings' height in Adirondacks, New York. Seedling's location, height, crown width, and diameter were measured from 16 fixed area sample plots, and multispectral imagery was acquired with DJI Matrice 100- UAV fitted with Micasense RedEdge-M camera. UAV was flown under clear sky conditions at 93-meter height in a single grid pattern with 80% front and side overlap. PIX4D software was used to process UAV multispectral imagery and generate Digital Surface Model (DSM) and Orthomosiac at 6.08 cm/pixel resolution along with 3D Digital Terrain Model (DTM). 3D densified point cloud layers of regeneration canopy were generated at an average density of 1.54 per m3. Seedlings were differentiated from bare ground cover through supervised image classification methods. Preliminary results of this study highlight that multispectral imagery acquired from UAVs has the potential to characterize and provide detailed structural information to estimate red pine seedlings' height.
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Suo, C., E. McGovern, and A. Gilmer. "VEGETATION MAPPING OF A COASTAL DUNE COMPLEX USING MULTISPECTRAL IMAGERY ACQUIRED FROM AN UNMANNED AERIAL SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 421–27. http://dx.doi.org/10.5194/isprs-archives-xlii-1-421-2018.
Повний текст джерелаАнотація:
<p><strong>Abstract.</strong> Vegetation mapping, identifying the distribution of plant species, is important for analysing vegetation dynamics, quantifying spatial patterns of vegetation evolution, analysing the effects of environment changes on vegetation, and predicting spatial patterns of species diversity. Such analysis can contribute to the development of targeted land management actions that maintain biodiversity and ecological functions. This paper represents a methodology for 3D vegetation mapping of a coastal dune complex using a multispectral camera mounted on an Unmanned Aerial System (UAS) with particular reference to the Buckroney dune complex in Co. Wicklow, Ireland. UAS, also known as Unmanned Aerial Vehicles (UAV’s) or drones, have enabled high-resolution and high-accuracy ground-based data to be gathered quickly and easily on-site. The Sequoia multispectral camera used in this study has green, red, red-edge and near infrared wavebands, and a normal RGB camera, to capture both visible and NIR images of the land surface. The workflow of 3D vegetation mapping of the study site included establishing ground control points, planning the flight mission and camera parameters, acquiring the imagery, processing the image data and performing features classification. The data processing outcomes include an orthomosiac model, a 3D surface model and multispectral images of the study site, in the Irish Transverse Mercator coordinate system, with a planimetric resolution of 0.024<span class="thinspace"></span>m and a georeferenced Root-Mean-Square (RMS) error of 0.111<span class="thinspace"></span>m. There were 235 sample area (1<span class="thinspace"></span>m<span class="thinspace"></span>&times;<span class="thinspace"></span>1<span class="thinspace"></span>m) used for the accuracy assessment of the classification of the vegetation mapping. Feature classification was conducted using three different classification strategies to examine the efficiency of multispectral sensor data for vegetation mapping. Vegetation type classification accuracies ranged from 60<span class="thinspace"></span>% to 70<span class="thinspace"></span>%. This research illustrates the efficiency of data collection at Buckroney dune complex and the high-accuracy and high-resolution of the vegetation mapping of the site using a multispectral sensor mounted on UAS.</p>
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Huang, Wenhui, Kun Wang, Yu An, Hui Meng, Yuan Gao, Zhiyuan Xiong, Hao Yan, et al. "In vivo three-dimensional evaluation of tumour hypoxia in nasopharyngeal carcinomas using FMT-CT and MSOT." European Journal of Nuclear Medicine and Molecular Imaging 47, no. 5 (November 8, 2019): 1027–38. http://dx.doi.org/10.1007/s00259-019-04526-x.
Повний текст джерелаАнотація:
Abstract Purpose Accurate evaluation of hypoxia is particularly important in patients with nasopharyngeal carcinoma (NPC) undergoing radiotherapy. The aim of this study was to propose a novel imaging strategy for quantitative three-dimensional (3D) evaluation of hypoxia in a small animal model of NPC. Methods A carbonic anhydrase IX (CAIX)-specific molecular probe (CAIX-800) was developed for imaging of hypoxia. Mouse models of subcutaneous, orthotopic, and spontaneous lymph node metastasis from NPC (5 mice per group) were established to assess the imaging strategy. A multi-modality imaging method that consisted of a hybrid combination of fluorescence molecular tomography-computed tomography (FMT-CT) and multispectral optoacoustic tomography (MSOT) was used for 3D quantitative evaluation of tumour hypoxia. Magnetic resonance imaging, histological examination, and immunohistochemical analysis were used as references for comparison and validation. Results In the early stage of NPC (2 weeks after implantation), FMT-CT enabled precise 3D localisation of the hypoxia biomarker with high sensitivity. At the advanced stage (6 weeks after implantation), MSOT allowed multispectral analysis of the biomarker and haemoglobin molecules with high resolution. The combination of high sensitivity and high resolution from FMT-CT and MSOT could not only detect hypoxia in small-sized NPCs but also visualise the heterogeneity of hypoxia in 3D. Conclusions Integration of FMT-CT and MSOT could allow comprehensive and quantifiable evaluation of hypoxia in NPC. These findings may potentially benefit patients with NPC undergoing radiotherapy in the future.
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Chromy, Adam, and Ludek Zalud. "The RoScan Thermal 3D Body Scanning System: Medical Applicability and Benefits for Unobtrusive Sensing and Objective Diagnosis." Sensors 20, no. 22 (November 20, 2020): 6656. http://dx.doi.org/10.3390/s20226656.
Повний текст джерелаАнотація:
The RoScan is a novel, high-accuracy multispectral surface scanning system producing colored 3D models that include a thermal layer. (1) Background: at present, medicine still exhibits a lack of objective diagnostic methods. As many diseases involve thermal changes, thermography may appear to be a convenient technique for the given purpose; however, there are three limiting problems: exact localization, resolution vs. range, and impossibility of quantification. (2) Methods: the basic principles and benefits of the system are described. The procedures rely on a robotic manipulator with multiple sensors to create a multispectral 3D model. Importantly, the structure is robust, scene-independent, and features quantifiable measurement uncertainty; thus, all of the above problems of medical thermography are resolved. (3) Results: the benefits were demonstrated by several pilot case studies: medicament efficacy assessment in dermatology, objective recovery progress assessment in traumatology, applied force quantification in forensic sciences, exact localization of the cause of pain in physiotherapy, objective assessment of atopic dermatitis, and soft tissue volumetric measurements. (4) Conclusion: the RoScan addresses medical quantification, which embodies a frequent problem in several medical sectors, and can deliver new, objective information to improve the quality of healthcare and to eliminate false diagnoses.
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Kolbe, Christine, Boris Thies, Sebastian Egli, Lukas Lehnert, Hans Schulz, and Jörg Bendix. "Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit—Part 1: Precipitation Area Delineation with Elektro-L2 and Insat-3D." Remote Sensing 11, no. 19 (October 2, 2019): 2302. http://dx.doi.org/10.3390/rs11192302.
Повний текст джерелаАнотація:
The lack of long term and well distributed precipitation observations on the Tibetan Plateau (TiP) with its complex terrain raises the need for other sources of precipitation data for this area. Satellite-based precipitation retrievals can fill those data gaps. Before precipitation rates can be retrieved from satellite imagery, the precipitating area needs to be classified properly. Here, we present a feasibility study of a precipitation area delineation scheme for the TiP based on multispectral data with data fusion from the geostationary orbit (GEO, Insat-3D and Elektro-L2) and a machine learning approach (Random Forest, RF). The GEO data are used as predictors for the RF model, extensively validated by independent GPM (Global Precipitation Measurement Mission) IMERG (Integrated Multi-satellitE Retrievals for GPM) gauge calibrated microwave (MW) best-quality precipitation estimates. To improve the RF model performance, we tested different optimization schemes. Here, we find that (1) using more precipitating pixels and reducing the amount of non-precipitating pixels during training greatly improved the classification results. The accuracy of the precipitation area delineation also benefits from (2) changing the temporal resolution into smaller segments. We particularly compared our results to the Infrared (IR) only precipitation product from GPM IMERG and found a markedly improved performance of the new multispectral product (Heidke Skill Score (HSS) of 0.19 (IR only) compared to 0.57 (new multispectral product)). Other studies with a precipitation area delineation obtained a probability of detection (POD) of 0.61, whereas our POD is comparable, with 0.56 on average. The new multispectral product performs best (worse) for precipitation rates above the 90th percentile (below the 10th percentile). Our results point to a clear strategy to improve the IMERG product in the absence of MW radiances.
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Martelet, Guillaume, Eric Gloaguen, Arne Døssing, Eduardo Lima Simoes da Silva, Johannes Linde, and Thorkild M. Rasmussen. "Airborne/UAV Multisensor Surveys Enhance the Geological Mapping and 3D Model of a Pseudo-Skarn Deposit in Ploumanac’h, French Brittany." Minerals 11, no. 11 (November 12, 2021): 1259. http://dx.doi.org/10.3390/min11111259.
Повний текст джерелаАнотація:
Taking advantage of a multi-sensor (multispectral and magnetic) drone survey, we address the detailed geological mapping and modeling of a mineralization in its geological environment. We stress that these high-resolution data allow us to bridge the gap between field observations and a regional aeromagnetic survey. On the one hand, the combination of multispectral imagery with field geological observations enhances detailed geological mapping. On the other hand, the combination of field magnetic susceptibility measurement and their use in detailed to regional magnetic modeling, constrained respectively by UAV-borne and airborne magnetic surveys, allows deriving a model of the mineralization consistent across the scales. This is demonstrated in a case study in a complex polyphased magmatic-metamorphic environment on the coast of French Brittany. The target area hosts a pseudo-skarn mineralization, exhibiting an outstanding magnetic anomaly. The combination of remotely sensed and field data allows deriving a realistic conceptual and geometrical model of the magnetic mineralization in its geological environment, tightly constrained by field observations and measurements.
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Vong, André, João P. Matos-Carvalho, Piero Toffanin, Dário Pedro, Fábio Azevedo, Filipe Moutinho, Nuno Cruz Garcia, and André Mora. "How to Build a 2D and 3D Aerial Multispectral Map?—All Steps Deeply Explained." Remote Sensing 13, no. 16 (August 13, 2021): 3227. http://dx.doi.org/10.3390/rs13163227.
Повний текст джерелаАнотація:
The increased development of camera resolution, processing power, and aerial platforms helped to create more cost-efficient approaches to capture and generate point clouds to assist in scientific fields. The continuous development of methods to produce three-dimensional models based on two-dimensional images such as Structure from Motion (SfM) and Multi-View Stereopsis (MVS) allowed to improve the resolution of the produced models by a significant amount. By taking inspiration from the free and accessible workflow made available by OpenDroneMap, a detailed analysis of the processes is displayed in this paper. As of the writing of this paper, no literature was found that described in detail the necessary steps and processes that would allow the creation of digital models in two or three dimensions based on aerial images. With this, and based on the workflow of OpenDroneMap, a detailed study was performed. The digital model reconstruction process takes the initial aerial images obtained from the field survey and passes them through a series of stages. From each stage, a product is acquired and used for the following stage, for example, at the end of the initial stage a sparse reconstruction is produced, obtained by extracting features of the images and matching them, which is used in the following step, to increase its resolution. Additionally, from the analysis of the workflow, adaptations were made to the standard workflow in order to increase the compatibility of the developed system to different types of image sets. Particularly, adaptations focused on thermal imagery were made. Due to the low presence of strong features and therefore difficulty to match features across thermal images, a modification was implemented, so thermal models could be produced alongside the already implemented processes for multispectral and RGB image sets.
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Es Sebar, Leila, Luca Lombardo, Paola Buscaglia, Tiziana Cavaleri, Alessandro Lo Giudice, Alessandro Re, Matilde Borla, Sara Aicardi, and Sabrina Grassini. "3D Multispectral Imaging for Cultural Heritage Preservation: The Case Study of a Wooden Sculpture of the Museo Egizio di Torino." Heritage 6, no. 3 (March 7, 2023): 2783–95. http://dx.doi.org/10.3390/heritage6030148.
Повний текст джерелаАнотація:
Digitalization techniques, such as photogrammetry (PG), are attracting the interest of experts in the cultural heritage field, as they enable the creation of three-dimensional virtual replicas of historical artifacts with 2D digital images. Indeed, PG allows for acquiring data regarding the overall appearance of an artifact, its geometry, and its texture. Furthermore, among several image-based techniques exploited for the conservation of works of art, multispectral imaging (MSI) finds great application in the study of the materials of historical items, taking advantage of the different responses of materials when exposed to specific wavelengths. Despite their great usefulness, PG and MSI are often used as separate tools. Integrating radiometric and geometrical data can notably expand the information carried by a 3D model. Therefore, this paper presents a novel research methodology that enables the acquisition of multispectral 3D models, combining the outcomes of PG and MSI (Visible (VIS), Ultraviolet-induced Visible Luminescence (UVL), Ultraviolet-Reflected (UVR), and Ultraviolet-Reflected False Color (UVR-FC) imaging) in a single coordinate system, using an affordable tunable set-up and open-source software. The approach has been employed for the study of two wooden artifacts from the Museo Egizio di Torino to investigate the materials present on the surface and provide information that could support the design of suitable conservation treatments.
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Godefroy, Guillaume, Bastien Arnal, and Emmanuel Bossy. "Multispectral photoacoustic fluctuation imaging for full visibility SO2 imaging." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A225. http://dx.doi.org/10.1121/10.0016088.
Повний текст джерелаАнотація:
Photoacoustic (PA) imaging (PAI) images blood vessels through the hemoglobin contrast. However, conventional images are affected by visibility artefacts which prevents seeing all the blood vessels morphology. We introduced PA fluctuation imaging (PAFI) exploiting the absorption fluctuations due to blood flow. Here, we demonstrate how PAFI enhances the image quality and elaborate if it can be used for quantitative multispectral (MS) imaging for 3D blood oxygenation (SO2) imaging. A spherical array (256 elements, 8 MHz, Imasonic) connected to a Verasonics Vantage scanner, was coupled to the laser (Innolas Spitlight) through a fiber bundle (Ceramoptek). The PAFI sequence consisted in acquiring 1000 frames at 100 Hz repetition rate while scanning continuously the wavelength from 680 nm to 880 nm. Image processing resulted in PAFI image per wavelength. The compensation of predictable effects of pertubations provided quantitative SO2 images with visibility and contrast enhancement. The technique was validated in glass capillaries and in the chicken embryo model by comparing the SO2 values with the one obtained in the visible structures using conventional imaging (resp. 3.5% and 9% errors). Thus, MS-PAFI can provide quantitative SO2 full-visibility 3D imaging with an intrinsic specificity to blood which simplifies spectral unmixing.
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Jackisch, Robert, Björn H. Heincke, Robert Zimmermann, Erik V. Sørensen, Markku Pirttijärvi, Moritz Kirsch, Heikki Salmirinne, Stefanie Lode, Urpo Kuronen, and Richard Gloaguen. "Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland." Solid Earth 13, no. 4 (April 7, 2022): 793–825. http://dx.doi.org/10.5194/se-13-793-2022.
Повний текст джерелаАнотація:
Abstract. Mineral exploration in the West Greenland flood basalt province is attractive because of its resemblance to the magmatic sulfide-rich deposit in the Russian Norilsk region, but it is challenged by rugged topography and partly poor exposure for relevant geologic formations. On northern Disko Island, previous exploration efforts have identified rare native iron occurrences and a high potential for Ni–Cu–Co–PGE–Au mineralization. However, Quaternary landslide activity has obliterated rock exposure in many places at lower elevations. To augment prospecting field work under these challenging conditions, we acquire high-resolution magnetic and multispectral remote sensing data using drones in the Qullissat area. From the data, we generate a detailed 3D model of a mineralized basalt unit, belonging to the Asuk Member of the Palaeocene Vaigat Formation. Different types of legacy data and newly acquired geo- and petrophysical as well as geochemical-mineralogical measurements form the basis of an integrated geological interpretation of the unoccupied aerial system (UAS) surveys. In this context, magnetic data aim to define the location and the shape of the buried magmatic body, and to estimate if its magnetic properties are indicative for mineralization. UAS-based multispectral orthomosaics are used to identify surficial iron staining, which serves as a proxy for outcropping sulfide mineralization. In addition, UAS-based digital surface models are created for geomorphological characterization of the landscape to accurately reveal landslide features. UAS-based magnetic data suggest that the targeted magmatic unit is characterized by a pattern of distinct positive and negative magnetic anomalies. We apply a 3D magnetization vector inversion (MVI) model to the UAS-based magnetic data to estimate the magnetic properties and shape of the magmatic body. By means of introducing constraints in the inversion, (1) UAS-based multispectral data and legacy drill cores are used to assign significant magnetic properties to areas that are associated with the mineralized Asuk Member, and (2) the Earth's magnetic and the palaeomagnetic field directions are used to evaluate the general magnetization direction in the magmatic units. Our results suggest that the geometry of the mineralized target can be estimated as a horizontal sheet of constant thickness, and that the magnetization of the unit has a strong remanent component formed during a period of Earth's magnetic field reversal. The magnetization values obtained in the MVI are in a similar range to the measured ones from a drillcore intersecting the targeted unit. Both the magnetics and topography confirm that parts of the target unit were displaced by landslides. We identified several fully detached and presumably rotated blocks in the obtained model. The model highlights magnetic anomalies that correspond to zones of mineralization and is used to identify outcrops for sampling. Our study demonstrates the potential and efficiency of using high-resolution UAS-based multi-sensor data to constrain the geometry of partially exposed geological units and assist exploration targeting in difficult or poorly exposed terrain.
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Zainuddin, K., Z. Majid, M. F. M. Ariff, and K. M. Idris. "MEASUREMENT ACCURACY ON 3D POINT CLOUD GENERATED USING MULTISPECTRAL IMAGERY BY DIFFERENT CALIBRATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (October 1, 2019): 697–703. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-697-2019.
Повний текст джерелаАнотація:
Abstract. The state-of-the-art lightweight multispectral cameras are widely used for low altitude remote sensing, also can be exploited as a tool for close-range photogrammetry application. The acquired imagery can be used for generating the 3D model using Structure-from-Motion/ Multi-view Stereo (SfM/MVS) processing software. In photogrammetry, camera calibration is an essential step for accurate measurement. The parameter of the camera system can be estimated using photogrammetric self-calibration bundle-adjustment, or by automatic and straightforward calibration procedure developed by computer vision (CV) community. When using SfM/MVS photogrammetry software, the pre-calibration value is not required, as the algorithm calculates the parameter as a part of point cloud construction process. Nevertheless, processing with the uncalibrated image is only suitable when no metric accuracy required in the modelling project. This paper aims to evaluate the measurement accuracy on generated 3D point cloud based on different estimated parameter method. The evaluation of measurement accuracy started by estimates the camera’s interior parameter using two different approaches; photogrammetric self-calibration bundle-adjustment and computer vision calibration. The estimated parameter from both methods then imported into commercial SfM/MVS software to construct the 3D point cloud. The point cloud also generated using uncalibrated images and used for measurement accuracy assessment. All parameters applied to the same datasets involved three different check-fields. Two accuracy assessments were performed by comparing the check-points and check-distance extracted with the total station measurement. As a result, the point cloud generated using photogrammetric approach provides the most accurate result on both assessments. While the automatic on-the-job self-calibration shows inconsistent results.
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Kalukin, Andrew, Satoshi Endo, Russell Crook, Manoj Mahajan, Robert Fennimore, Alice Cialella, Laurie Gregory, Shinjae Yoo, Wei Xu, and Daniel Cisek. "Image Collection Simulation Using High-Resolution Atmospheric Modeling." Remote Sensing 12, no. 19 (October 1, 2020): 3214. http://dx.doi.org/10.3390/rs12193214.
Повний текст джерелаАнотація:
A new method is described for simulating the passive remote sensing image collection of ground targets that includes effects from atmospheric physics and dynamics at fine spatial and temporal scales. The innovation in this research is the process of combining a high-resolution weather model with image collection simulation to attempt to account for heterogeneous and high-resolution atmospheric effects on image products. The atmosphere was modeled on a 3D voxel grid by a Large-Eddy Simulation (LES) driven by forcing data constrained by local ground-based and air-based observations. The spatial scale of the atmospheric model (10–100 m) came closer than conventional weather forecast scales (10–100 km) to approaching the scale of typical commercial multispectral imagery (2 m). This approach was demonstrated through a ground truth experiment conducted at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. In this experiment, calibrated targets (colored spectral tarps) were placed on the ground, and the scene was imaged with WorldView-3 multispectral imagery at a resolution enabling the tarps to be visible in at least 9–12 image pixels. The image collection was simulated with Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, using the 3D atmosphere from the LES model to generate a high-resolution cloud mask. The high-resolution atmospheric model-predicted cloud coverage was usually within 23% of the measured cloud cover. The simulated image products were comparable to the WorldView-3 satellite imagery in terms of the variations of cloud distributions and spectral properties of the ground targets in clear-sky regions, suggesting the potential utility of the proposed modeling framework in improving simulation capabilities, as well as testing and improving the operation of image collection processes.
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Cheng, Jinpeng, Hao Yang, Jianbo Qi, Zhendong Sun, Shaoyu Han, Haikuan Feng, Jingyi Jiang, et al. "Estimating canopy-scale chlorophyll content in apple orchards using a 3D radiative transfer model and UAV multispectral imagery." Computers and Electronics in Agriculture 202 (November 2022): 107401. http://dx.doi.org/10.1016/j.compag.2022.107401.
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Bakuła, K., P. Kupidura, and Ł. Jełowicki. "TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 20, 2016): 161–69. http://dx.doi.org/10.5194/isprs-archives-xli-b7-161-2016.
Повний текст джерелаАнотація:
Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return numbers slightly improved the results.
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Bakuła, K., P. Kupidura, and Ł. Jełowicki. "TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 20, 2016): 161–69. http://dx.doi.org/10.5194/isprsarchives-xli-b7-161-2016.
Повний текст джерелаАнотація:
Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return numbers slightly improved the results.
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Gong, Chizhang, Henning Buddenbaum, Rebecca Retzlaff, and Thomas Udelhoven. "An Empirical Assessment of Angular Dependency for RedEdge-M in Sloped Terrain Viticulture." Remote Sensing 11, no. 21 (October 31, 2019): 2561. http://dx.doi.org/10.3390/rs11212561.
Повний текст джерелаАнотація:
For grape canopy pixels captured by an unmanned aerial vehicle (UAV) tilt-mounted RedEdge-M multispectral sensor in a sloped vineyard, an in situ Walthall model can be established with purely image-based methods. This was derived from RedEdge-M directional reflectance and a vineyard 3D surface model generated from the same imagery. The model was used to correct the angular effects in the reflectance images to form normalized difference vegetation index (NDVI) orthomosaics of different view angles. The results showed that the effect could be corrected to a certain scope, but not completely. There are three drawbacks that might restrict a successful angular model construction and correction: (1) the observable micro shadow variation on the canopy enabled by the high resolution; (2) the complexity of vine canopies that causes an inconsistency between reflectance and canopy geometry, including effects such as micro shadows and near-infrared (NIR) additive effects; and (3) the resolution limit of a 3D model to represent the accurate real-world optical geometry. The conclusion is that grape canopies might be too inhomogeneous for the tested method to perform the angular correction in high quality.
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Zhu, Sun, Peng, Huang, Li, Zhang, Yang, and Liao. "Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales." Remote Sensing 11, no. 22 (November 15, 2019): 2678. http://dx.doi.org/10.3390/rs11222678.
Повний текст джерелаАнотація:
Crop above-ground biomass (AGB) is a key parameter used for monitoring crop growth and predicting yield in precision agriculture. Estimating the crop AGB at a field scale through the use of unmanned aerial vehicles (UAVs) is promising for agronomic application, but the robustness of the methods used for estimation needs to be balanced with practical application. In this study, three UAV remote sensing flight missions (using a multiSPEC-4C multispectral camera, a Micasense RedEdge-M multispectral camera, and an Alpha Series AL3-32 Light Detection and Ranging (LiDAR) sensor onboard three different UAV platforms) were conducted above three long-term experimental plots with different tillage treatments in 2018. We investigated the performances of the multi-source UAV-based 3D point clouds at multi-spatial scales using the traditional multi-variable linear regression model (OLS), random forest (RF), backpropagation neural network (BP), and support vector machine (SVM) methods for accurate AGB estimation. Results showed that crop height (CH) was a robust proxy for AGB estimation, and that high spatial resolution in CH datasets helps to improve maize AGB estimation. Furthermore, the OLS, RF, BP, and SVM methods all maintained an acceptable accuracy for AGB estimation; however, the SVM and RF methods performed slightly more robustly. This study is expected to optimize UAV systems and algorithms for specific agronomic applications.
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Takács, Péter, János Tamás, and Csaba Lénárd. "Virtual Soil Information Systems in the Bihar Subregion and at Tedej Corp." Acta Agraria Debreceniensis, no. 13 (May 4, 2004): 186–89. http://dx.doi.org/10.34101/actaagrar/13/3410.
Повний текст джерелаАнотація:
After evaluating the sample sites’ soils and environmental status, we built up 2 different soil information systems. The first relies on analog data (soil maps), and is based on a regional model; its sample site is the Bihar sub-region. The second is a complex, field scale virtual 3D system, based on several types of data sources. (Aerial photos, GPS, field samples, hyper and multispectral images, soil maps). In this paper, we analyze and evaluate these systems. The greatest advantage of the models is that, with their usage, we can reveal connections which cannot be made by analyzing the individual elements of our data sources. We discovered that with the help of our systems, the monitoring and evaluating of the processes taking place in the soil is more fast and simple.
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Cardaci, A., A. Versaci, and P. Azzola. "3D LOW-COST MULTISPECTRAL UAV SYSTEMS: SURVEY AND ANALYSIS OF TORRESINO DA POLVERE OF SAN MARCO IN BERGAMO." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W1-2022 (December 8, 2022): 37–44. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-w1-2022-37-2022.
Повний текст джерелаАнотація:
Abstract. The Torresini da Polvere are special constructions built by the Venetian Republic between the 16th and 18th centuries. They are unique architectural structures, characterized by a pyramidal roof, for the preservation of the gunpowder. The torresino of San Marco in Bergamo is one of the best-preserved in the world and was then the subject of detailed research. The powder magazine was measured by professional, ground-based and airborne instruments. The paper shows a comparison between the existing structure and the photogrammetric model obtained by low-cost UAV systems to formulate an accurate estimate, including the pros and cons of different survey systems. It also aims to deepen the aspects associated with 3D reconstruction using multispectral imagery, from the investigation to data processing, to create models with NDVI mapping for the study of the building’s decay caused by biological agents.
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PENG, YAJING, YANXUE JIANG, and YANQIANG YANG. "LASER-INDUCED THERMAL–MECHANICAL DAMAGE CHARACTERISTICS OF CLEARTRAN MULTISPECTRAL ZINC SULFIDE WITH TEMPERATURE-DEPENDENT PROPERTIES." Surface Review and Letters 22, no. 01 (February 2015): 1550014. http://dx.doi.org/10.1142/s0218625x15500146.
Повний текст джерелаАнотація:
Laser-induced thermal–mechanical damage characteristics of window materials are the focus problems in laser weapon and anti-radiation reinforcement technology. Thermal–mechanical effects and damage characteristics are investigated for cleartran multispectral zinc sulfide ( ZnS ) thin film window materials irradiated by continuous laser using three-dimensional (3D) thermal–mechanical model. Some temperature-dependent parameters are introduced into the model. The temporal-spatial distributions of temperature and thermal stress are exhibited. The damage mechanism is analyzed. The influences of temperature effect of material parameters and laser intensity on the development of thermal stress and the damage characteristics are examined. The results show, the von Mises equivalent stress along the thickness direction is fluctuant, which originates from the transformation of principal stresses from compressive stress to tensile stress with the increase of depth from irradiated surface. The damage originates from the thermal stress but not the melting. The thermal stress is increased and the damage is accelerated by introducing the temperature effect of parameters or the increasing laser intensity.
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Dou, Xinyu, Chenyu Li, Qian Shi, and Mengxi Liu. "Super-Resolution for Hyperspectral Remote Sensing Images Based on the 3D Attention-SRGAN Network." Remote Sensing 12, no. 7 (April 8, 2020): 1204. http://dx.doi.org/10.3390/rs12071204.
Повний текст джерелаАнотація:
Hyperspectral remote sensing images (HSIs) have a higher spectral resolution compared to multispectral remote sensing images, providing the possibility for more reasonable and effective analysis and processing of spectral data. However, rich spectral information usually comes at the expense of low spatial resolution owing to the physical limitations of sensors, which brings difficulties for identifying and analyzing targets in HSIs. In the super-resolution (SR) field, many methods have been focusing on the restoration of the spatial information while ignoring the spectral aspect. To better restore the spectral information in the HSI SR field, a novel super-resolution (SR) method was proposed in this study. Firstly, we innovatively used three-dimensional (3D) convolution based on SRGAN (Super-Resolution Generative Adversarial Network) structure to not only exploit the spatial features but also preserve spectral properties in the process of SR. Moreover, we used the attention mechanism to deal with the multiply features from the 3D convolution layers, and we enhanced the output of our model by improving the content of the generator’s loss function. The experimental results indicate that the 3DASRGAN (3D Attention-based Super-Resolution Generative Adversarial Network) is both visually quantitatively better than the comparison methods, which proves that the 3DASRGAN model can reconstruct high-resolution HSIs with high efficiency.
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Angheluță, L. M., L. Ratoiu, A. I. Chelmus, R. Rădvan, and A. Petculescu. "INTEGRATED DIGITAL PLATFORM FOR THE VALORIZATION OF A CULTURAL LANDSCAPE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5/W1 (May 16, 2017): 389–93. http://dx.doi.org/10.5194/isprs-archives-xlii-5-w1-389-2017.
Повний текст джерелаАнотація:
This paper presents a newly started demonstrative project regarding the implementation and validation of an interdisciplinary research model for the Aluniș-Bozioru (Romania) cultural landscape, with the development of an online interactive digital product. This digital product would provide complementary data about the historical monuments and their environment, and also, constant updates and statistical comparison in order to generate an accurate evaluation of the state of conservation for this specific cultural landscape. Furthermore, the resulted information will contribute in the decision making process for the regional development policies. The project is developed by an interdisciplinary joint team of researchers consisted of technical scientists with great experience in advanced non-invasive characterization of the cultural heritage (NIRD for Optoelectronics – INOE 2000) and a group of experts from geology and biology (Romanian Academy’s “Emil Racoviță” Institute of Speleology – ISER). Resulted scientific data will include: 3D digital models of the selected historical monuments, microclimate monitoring, Ground Penetrating Radar survey, airborne LIDAR, multispectral and thermal imaging, soil and rock characterization, environmental studies. This digital product is constituted by an intuitive website with a database that allows data corroboration, visualization and comparison of the 3D digital models, as well as a digital mapping in the GIS system.
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Nurtyawan, Rian, and Nadia Fiscarina. "ASSESSMENT OF THE ACCURACY OF DEM FROM PANCHROMATIC PLEIADES IMAGERY (CASE STUDY: BANDUNG CITY. WEST JAVA)." International Journal of Remote Sensing and Earth Sciences (IJReSES) 17, no. 1 (August 20, 2020): 34. http://dx.doi.org/10.30536/j.ijreses.2020.v17.a3329.
Повний текст джерелаАнотація:
Pleiades satellite imagery is very high resolution. with 0.5 m spatial resolution in the panchromatic band and 2.5 m in the multispectral band. Digital elevation models (DEM) are digital models that represent the shape of the Earth's surface in three-dimensional (3D) form. The purpose of this study was to assess DEM accuracy from panchromatic Pleaides imagery. The process conducted was orthorectification using ground control points (GCPs) and the rational function model with rational polynomial coefficient (RFC) parameters. The DEM extraction process employed photogrammetric methods with different parallax concepts. Accuracy assessment was made using 35 independent check points (ICPs) with an RMSE accuracy of ± 0.802 m. The results of the Pleaides DEM image extraction were more accurate than the National DEM (DEMNAS) and SRTM DEM. Accuracy testing of DEMNAS results showed an RMSE of ± 0.955 m. while SRTM DEM accuracy was ± 17.740 m. Such DEM extraction from stereo Pleiades panchromatic images can be used as an element on base maps with a scale of 1: 5.000.
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Rüdisser, Daniel, Tobias Weiss, and Lukas Unger. "Spatially Resolved Analysis of Urban Thermal Environments Based on a Three-Dimensional Sampling Algorithm and UAV-Based Radiometric Measurements." Sensors 21, no. 14 (July 16, 2021): 4847. http://dx.doi.org/10.3390/s21144847.
Повний текст джерелаАнотація:
A new method and workflow to assess outdoor thermal comfort and thermal stress in urban areas is developed. The new methodology is applied to a case of an urban quarter in the city of Graz. The method recognises the significance of detailed and accurate spatially resolved determination of mean radiant temperatures taking into account all relevant radiative components, comprising thermal radiation, as well as global radiation. The method relies on radiometric imaging data that are mapped onto a three-dimensional model. The image data are acquired by means of drones (UAVs) equipped with multispectral and thermographic cameras to capture short- and long-wave radiation. Pre-existing city models and a Monte Carlo raytracing algorithm to perform anisotropic sampling based on a 3D model with human topology are used to determine local radiation temperatures with high spatial resolution. Along with spot measurements carried out on the ground simultaneously, the spatially resolved and three-dimensionally determined mean radiation temperatures are used to calculate thermal comfort indicator maps using UTCI and PMV calculation. Additional ground measurements are further used to validate the detection, as well as the entire evaluation process.
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Sangjan, Worasit, Rebecca J. McGee, and Sindhuja Sankaran. "Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage Crop." Remote Sensing 14, no. 10 (May 17, 2022): 2396. http://dx.doi.org/10.3390/rs14102396.
Повний текст джерелаАнотація:
Forage and field peas provide essential nutrients for livestock diets, and high-quality field peas can influence livestock health and reduce greenhouse gas emissions. Above-ground biomass (AGBM) is one of the vital traits and the primary component of yield in forage pea breeding programs. However, a standard method of AGBM measurement is a destructive and labor-intensive process. This study utilized an unmanned aerial vehicle (UAV) equipped with a true-color RGB and a five-band multispectral camera to estimate the AGBM of winter pea in three breeding trials (two seed yields and one cover crop). Three processing techniques—vegetation index (VI), digital surface model (DSM), and 3D reconstruction model from point clouds—were used to extract the digital traits (height and volume) associated with AGBM. The digital traits were compared with the ground reference data (measured plant height and harvested AGBM). The results showed that the canopy volume estimated from the 3D model (alpha shape, α = 1.5) developed from UAV-based RGB imagery’s point clouds provided consistent and high correlation with fresh AGBM (r = 0.78–0.81, p < 0.001) and dry AGBM (r = 0.70–0.81, p < 0.001), compared with other techniques across the three trials. The DSM-based approach (height at 95th percentile) had consistent and high correlation (r = 0.71–0.95, p < 0.001) with canopy height estimation. Using the UAV imagery, the proposed approaches demonstrated the potential for estimating the crop AGBM across winter pea breeding trials.
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Ilehag, R., D. Bulatov, P. Helmholz, and D. Belton. "CLASSIFICATION AND REPRESENTATION OF COMMONLY USED ROOFING MATERIAL USING MULTISENSORIAL AERIAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 217–24. http://dx.doi.org/10.5194/isprs-archives-xlii-1-217-2018.
Повний текст джерелаАнотація:
<p><strong>Abstract.</strong> As more cities are starting to experience the urban heat islands effect, knowledge about the energy emitted from building roofs is of primary importance. Since this energy depends both on roof orientations and materials, we tackled both issues by analysing sensor data from multispectral, thermal infrared, high-resolution RGB, and airborne laser datasets (each with different spatial resolutions) of a council in Perth, Australia. To localise the roofs, we acquired building outlines that had to be updated using the normalised digital surface model, the NDVI and the planarity. Then, we computed a semantic 3D model of the study area, with roof detail analysis being a particular focus. The main objective of this study, however, was to classify three commonly used roofing materials: <i>Cement tiles</i>, <i>Colorbond</i> and <i>Zincalume</i> by combining the multispectral and thermal infrared image bands while the high-resolution RGB dataset was used to provide additional information about the roof texture. Three types of image segmentation approaches were evaluated to assess any differences while performing the material classification; pixel-wise, superpixel-wise and building-wise image segmentation. Due to the limited amount of labelled data, we extended the dataset by labelling data ourselves and merged <i>Colorbond</i> and <i>Zincalume</i> into one separate class. The supervised classifier Random Forest was applied to all reasonable configurations of segmentation kinds, numbers of classes, and finally, keeping track of the added value of principal component analysis.</p>
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Comba, L., A. Biglia, D. Ricauda Aimonino, C. Tortia, E. Mania, S. Guidoni, and P. Gay. "Leaf Area Index evaluation in vineyards using 3D point clouds from UAV imagery." Precision Agriculture 21, no. 4 (November 27, 2019): 881–96. http://dx.doi.org/10.1007/s11119-019-09699-x.
Повний текст джерелаАнотація:
AbstractThe Leaf Area Index (LAI) is an ecophysiology key parameter characterising the canopy-atmosphere interface where most of the energy fluxes are exchanged. However, producing maps for managing the spatial and temporal variability of LAI in large croplands with traditional techniques is typically laborious and expensive. The objective of this paper is to evaluate the reliability of LAI estimation by processing dense 3D point clouds as a cost-effective alternative to traditional LAI assessments. This would allow for high resolution, extensive and fast mapping of the index, even in hilly and not easily accessible regions. In this setting, the 3D point clouds were generated from UAV-based multispectral imagery and processed by using an innovative methodology presented here. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for canopy thickness, height and leaf density distribution along the wall. For the validation of the estimated LAI, an experiment was conducted in a vineyard in Piedmont: the leaf area of 704 vines was manually measured by the inclined point quadrant approach and six UAV flights were contextually performed to acquire the aerial images. The vineyard LAI estimated by the proposed methodology showed to be correlated with the ones obtained by the traditional manual method. Indeed, the obtained R2 value of 0.82 can be considered fully adequate, compatible to the accuracy of the reference LAI manual measurement.
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Näsi, R., N. Viljanen, J. Kaivosoja, T. Hakala, M. Pandžić, L. Markelin, and E. Honkavaara. "ASSESSMENT OF VARIOUS REMOTE SENSING TECHNOLOGIES IN BIOMASS AND NITROGEN CONTENT ESTIMATION USING AN AGRICULTURAL TEST FIELD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W3 (October 19, 2017): 137–41. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w3-137-2017.
Повний текст джерелаАнотація:
Multispectral and hyperspectral imaging is usually acquired by satellite and aircraft platforms. Recently, miniaturized hyperspectral 2D frame cameras have showed great potential to precise agriculture estimations and they are feasible to combine with lightweight platforms, such as drones. Drone platform is a flexible tool for remote sensing applications with environment and agriculture. The assessment and comparison of different platforms such as satellite, aircraft and drones with different sensors, such as hyperspectral and RGB cameras is an important task in order to understand the potential of the data provided by these equipment and to select the most appropriate according to the user applications and requirements. In this context, open and permanent test fields are very significant and helpful experimental environment, since they provide a comparative data for different platforms, sensors and users, allowing multi-temporal analyses as well. Objective of this work was to investigate the feasibility of an open permanent test field in context of precision agriculture. Satellite (Sentinel-2), aircraft and drones with hyperspectral and RGB cameras were assessed in this study to estimate biomass, using linear regression models and in-situ samples. Spectral data and 3D information were used and compared in different combinations to investigate the quality of the models. The biomass estimation accuracies using linear regression models were better than 90&thinsp;% for the drone based datasets. The results showed that the use of spectral and 3D features together improved the estimation model. However, estimation of nitrogen content was less accurate with the evaluated remote sensing sensors. The open and permanent test field showed to be suitable to provide an accurate and reliable reference data for the commercial users and farmers.
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Khare, Siddhartha, Hooman Latifi, Sergio Rossi, and Sanjay Kumar Ghosh. "Fractional Cover Mapping of Invasive Plant Species by Combining Very High-Resolution Stereo and Multi-Sensor Multispectral Imageries." Forests 10, no. 7 (June 27, 2019): 540. http://dx.doi.org/10.3390/f10070540.
Повний текст джерелаАнотація:
Invasive plant species are major threats to biodiversity. They can be identified and monitored by means of high spatial resolution remote sensing imagery. This study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial resolutions of 1.5 and 5 m to quantify the presence of the invasive lantana (Lantana camara L.) and predict its distribution at large spatial scale using medium-resolution fractional cover analysis. We created initial training data for fractional cover analysis by classifying smaller extent VHR data (SPOT-6 and RapidEye) along with three dimensional (3D) VHRSI derived digital surface model (DSM) datasets. We modelled the statistical relationship between fractional cover and spectral reflectance for a VHR subset of the study area located in the Himalayan region of India, and finally predicted the fractional cover of lantana based on the spectral reflectance of Landsat-8 imagery of a larger spatial extent. We classified SPOT-6 and RapidEye data and used the outputs as training data to create continuous field layers of Landsat-8 imagery. The area outside the overlapping region was predicted by fractional cover analysis due to the larger extent of Landsat-8 imagery compared with VHR datasets. Results showed clear discrimination of understory lantana from upperstory vegetation with 87.38% (for SPOT-6), and 85.27% (for RapidEye) overall accuracy due to the presence of additional VHRSI derived DSM information. Independent validation for lantana fractional cover estimated root-mean-square errors (RMSE) of 11.8% (for RapidEye) and 7.22% (for SPOT-6), and R2 values of 0.85 and 0.92 for RapidEye (5 m) and SPOT-6 (1.5 m), respectively. Results suggested an increase in predictive accuracy of lantana within forest areas along with increase in the spatial resolution for the same Landsat-8 imagery. The variance explained at 1.5 m spatial resolution to predict lantana was 64.37%, whereas it decreased by up to 37.96% in the case of 5 m spatial resolution data. This study revealed the high potential of combining small extent VHR and VHRSI- derived 3D optical data with larger extent, freely available satellite data for identification and mapping of invasive species in mountainous forests and remote regions.
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Esch, Thomas, Julian Zeidler, Daniela Palacios-Lopez, Mattia Marconcini, Achim Roth, Milena Mönks, Benjamin Leutner, et al. "Towards a Large-Scale 3D Modeling of the Built Environment—Joint Analysis of TanDEM-X, Sentinel-2 and Open Street Map Data." Remote Sensing 12, no. 15 (July 25, 2020): 2391. http://dx.doi.org/10.3390/rs12152391.
Повний текст джерелаАнотація:
Continental to global scale mapping of the human settlement extent based on earth observation satellite data has made considerable progress. Nevertheless, the current approaches only provide a two-dimensional representation of the built environment. Therewith, a full characterization is restricted in terms of the urban morphology and built-up density, which can only be gained by a detailed examination of the vertical settlement extent. This paper introduces a methodology for the extraction of three-dimensional (3D) information on human settlements by analyzing the digital elevation and radar intensity data collected by the German TanDEM-X satellite mission in combination with multispectral Sentinel-2 imagery and data from the Open Street Map initiative and the Global Urban Footprint human settlement mask. The first module of the underlying processor generates a normalized digital surface model from the TanDEM-X digital elevation model for all regions marked as a built-up area by the Global Urban Footprint. The second module generates a building mask based on a joint processing of Open Street Map, TanDEM-X/TerraSAR-X radar images, the calculated normalized digital surface model and Sentinel-2 imagery. Finally, a third module allocates the local relative heights of the normalized digital surface model to the building structures provided by the building mask. The outcome of the procedure is a 3D map of the built environment showing the estimated local height for all identified vertical building structures at 12 m spatial resolution. The results of a first validation campaign based on reference data collected for the seven cities of Amsterdam (NL), Indianapolis (US), Kigali (RW), Munich (DE), New York (US), Vienna (AT), and Washington (US) indicate the potential of the proposed methodology to accurately estimate the distribution of building heights within the built-up area.
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Michez, Adrien, Philippe Lejeune, Sébastien Bauwens, Andriamandroso Herinaina, Yannick Blaise, Eloy Castro Muñoz, Frédéric Lebeau, and Jérôme Bindelle. "Mapping and Monitoring of Biomass and Grazing in Pasture with an Unmanned Aerial System." Remote Sensing 11, no. 5 (February 26, 2019): 473. http://dx.doi.org/10.3390/rs11050473.
Повний текст джерелаАнотація:
The tools available to farmers to manage grazed pastures and adjust forage demand to grass growth are generally rather static. Unmanned aerial systems (UASs) are interesting versatile tools that can provide relevant 3D information, such as sward height (3D structure), or even describe the physical condition of pastures through the use of spectral information. This study aimed to evaluate the potential of UAS to characterize a pasture’s sward height and above-ground biomass at a very fine spatial scale. The pasture height provided by UAS products showed good agreement (R2 = 0.62) with a reference terrestrial light detection and ranging (LiDAR) dataset. We tested the ability of UAS imagery to model pasture biomass based on three different combinations: UAS sward height, UAS sward multispectral reflectance/vegetation indices, and a combination of both UAS data types. The mixed approach combining the UAS sward height and spectral data performed the best (adj. R2 = 0.49). This approach reached a quality comparable to that of more conventional non-destructive on-field pasture biomass monitoring tools. As all of the UAS variables used in the model fitting process were extracted from spatial information (raster data), a high spatial resolution map of pasture biomass was derived based on the best fitted model. A sward height differences map was also derived from UAS-based sward height maps before and after grazing. Our results demonstrate the potential of UAS imagery as a tool for precision grazing study applications. The UAS approach to height and biomass monitoring was revealed to be a potential alternative to the widely used but time-consuming field approaches. While reaching a similar level of accuracy to the conventional field sampling approach, the UAS approach provides wall-to-wall pasture characterization through very high spatial resolution maps, opening up a new area of research for precision grazing.
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Oniga, Valeria-Ersilia, Norbert Pfeifer, and Ana-Maria Loghin. "3D Calibration Test-Field for Digital Cameras Mounted on Unmanned Aerial Systems (UAS)." Remote Sensing 10, no. 12 (December 12, 2018): 2017. http://dx.doi.org/10.3390/rs10122017.
Повний текст джерелаАнотація:
Due to the large number of technological developments in recent years, UAS systems are now used for monitoring purposes and in projects with high precision demand, such as 3D model-based creation of dams, reservoirs, historical monuments etc. These unmanned systems are usually equipped with an automatic pilot device and a digital camera (photo/video, multispectral, Near Infrared etc.), of which the lens has distortions; but this can be determined in a calibration process. Currently, a method of “self-calibration” is used for the calibration of the digital cameras mounted on UASs, but, by using the method of calibration based on a 3D calibration object, the accuracy is improved in comparison with other methods. Thus, this paper has the objective of establishing a 3D calibration field for the digital cameras mounted on UASs in terms of accuracy and robustness, being the largest reported publication to date. In order to test the proposed calibration field, a digital camera mounted on a low-cost UAS was calibrated at three different heights: 23 m, 28 m, and 35 m, using two configurations for image acquisition. Then, a comparison was made between the residuals obtained for a number of 100 Check Points (CPs) using self-calibration and test-field calibration, while the number of Ground Control Points (GCPs) variedand the heights were interchanged. Additionally, the parameters where tested on an oblique flight done 2 years before calibration, in manual mode at a medium altitude of 28 m height. For all tests done in the case of the double grid nadiral flight, the parameters calculated with the proposed 3D field improved the results by more than 50% when using the optimum and a large number of GCPs, and in all analyzed cases with 75% to 95% when using a minimum of 3 GCP. In this context, it is necessary to conduct accurate calibration in order to increase the accuracy of the UAS projects, and also to reduce field measurements.
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Jurado, J., M. Ramos, C. Enríquez, and F. Feito. "The Impact of Canopy Reflectance on the 3D Structure of Individual Trees in a Mediterranean Forest." Remote Sensing 12, no. 9 (May 1, 2020): 1430. http://dx.doi.org/10.3390/rs12091430.
Повний текст джерелаАнотація:
The characterization of 3D vegetation structures is an important topic, which has been addressed by recent research in remote sensing. The forest inventory requires the proper extraction of accurate structural and functional features of individual trees. This paper presents a novel methodology to study the impact of the canopy reflectance on the 3D tree structure. A heterogeneous natural environment in a Mediterranean forest, in which various tree species (pine, oak and eucalyptus) coexist, was covered using a high-resolution digital camera and a multispectral sensor. These devices were mounted on an Unmanned Aerial Vehicle (UAV) in order to observe the tree architecture and the spectral reflectance at the same time. The Structure from Motion (SfM) method was applied to model the 3D structures using RGB images from the high-resolution camera. The geometric accuracy of the resulting point cloud was validated by georeferencing the study area through multiple ground control points (GCPs). Then, the point cloud was enriched with the reflected light in four narrow-bands (green, near-infrared, red and red-edge). Furthermore, the Normalized Difference Vegetation Index (NDVI) was calculated in order to measure the tree vigor. A comprehensive analysis based on structural and spectral features of individual trees was proposed. A spatial segmentation was developed to detect single-trees in a forest and for each one to identify the crown and trunk. Consequently, structural parameters were extracted, such as the tree height, the diameter at breast height (DBH) and the crown volume. The validation of these measurements was performed by field data, which were taken using a Total Station (TS). In addition, these characteristics were correlated with the mean reflectance in the tree canopy. Regarding the observed tree species, a statistical analysis was carried out to study the impact of reflectance on the 3D tree structure. By applying our method, a more detailed knowledge of forest dynamics can be gained and the impact of available solar irradiance on single-trees can be analyzed.
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Yang, Zhiwen, Hebing Zhang, Xiaoxuan Lyu, and Weibing Du. "Improving Typical Urban Land-Use Classification with Active-Passive Remote Sensing and Multi-Attention Modules Hybrid Network: A Case Study of Qibin District, Henan, China." Sustainability 14, no. 22 (November 8, 2022): 14723. http://dx.doi.org/10.3390/su142214723.
Повний текст джерелаАнотація:
The study of high-precision land-use classification is essential for the sustainable development of land resources. This study addresses the problem of classification errors in optical remote-sensing images under high surface humidity, cloud cover, and hazy weather. The synthetic aperture radar (SAR) images are sensitive to soil moisture, and the microwave can penetrate clouds, haze, and smoke. By using both the active and passive remote-sensing data, the Sentinel-1A SAR and Sentinel-2B multispectral (MS) images are combined synergistically. The full-band data combining the SAR + MS + spectral indexes is thus constructed. Based on the high dimensionality and heterogeneity of this data set, a new framework (MAM-HybridNet) based on two-dimensional (2D) and three-dimensional (3D) hybrid convolutional neural networks combined with multi-attention modules (MAMs) is proposed for improving the accuracy of land-use classification in cities with high surface humidity. In addition, the same training samples supported by All bands data (SAR + MS + spectral index) are selected and compared with k-Nearest Neighbors (KNN), support vector machine (SVM), 2D convolutional neural networks, 3D convolutional neural networks, and hybridSN classification models to verify the accuracy of the proposed classification model. The results show that (1) fusion classification based on Sentinel-2B MSI and Sentinel-1A SAR data produce an overall accuracy (OA) of 95.10%, a kappa coefficient (KC) of 0.93, and an average accuracy (AA) of 92.86%, which is better than the classification results using Sentinel-2B MSI and Sentinel-1A SAR images separately. (2) The classification accuracy improves upon adding the spectral index, and the OA, KC, and AA improve by 3.77%, 0.05, and 5.5%, respectively. (3) With the support of full-band data, the algorithm proposed herein produces better results than other classification algorithms, with an OA of 98.87%, a KC of 0.98, and an AA of 98.36%. These results indicate that the synergistic effect of active-passive remote-sensing data improves land-use classification. Additionally, the results verify the effectiveness of the proposed deep-learning classification model for land-use classification.
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Deng, S., M. Katoh, Y. Takenaka, K. Cheung, A. Ishii, N. Fujii, and T. Gao. "TREE SPECIES CLASSIFICATION OF BROADLEAVED FORESTS IN NAGANO, CENTRAL JAPAN, USING AIRBORNE LASER DATA AND MULTISPECTRAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W3 (October 19, 2017): 33–38. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w3-33-2017.
Повний текст джерелаАнотація:
This study attempted to classify three coniferous and ten broadleaved tree species by combining airborne laser scanning (ALS) data and multispectral images. The study area, located in Nagano, central Japan, is within the broadleaved forests of the Afan Woodland area. A total of 235 trees were surveyed in 2016, and we recorded the species, DBH, and tree height. The geographical position of each tree was collected using a Global Navigation Satellite System (GNSS) device. Tree crowns were manually detected using GNSS position data, field photographs, true-color orthoimages with three bands (red-green-blue, RGB), 3D point clouds, and a canopy height model derived from ALS data. Then a total of 69 features, including 27 image-based and 42 point-based features, were extracted from the RGB images and the ALS data to classify tree species. Finally, the detected tree crowns were classified into two classes for the first level (coniferous and broadleaved trees), four classes for the second level (<i>Pinus densiflora</i>, <i>Larix kaempferi</i>, <i>Cryptomeria japonica</i>, and broadleaved trees), and 13 classes for the third level (three coniferous and ten broadleaved species), using the 27 image-based features, 42 point-based features, all 69 features, and the best combination of features identified using a neighborhood component analysis algorithm, respectively. The overall classification accuracies reached 90&thinsp;% at the first and second levels but less than 60&thinsp;% at the third level. The classifications using the best combinations of features had higher accuracies than those using the image-based and point-based features and the combination of all of the 69 features.