Journal articles on the topic '3D Remote Sensing data'
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Rautji, Surbhi, Deepak Gaur, and Karan Khare. "Immersive 3D Visualization of Remote Sensing Data." Signal & Image Processing : An International Journal 4, no. 5 (November 2013): 61–73. http://dx.doi.org/10.5121/sipij.2013.4505.
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Xu, Yuanjin. "Application of Remote Sensing Image Data Scene Generation Method in Smart City." Complexity 2021 (January 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/6653841.
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Remote sensing image simulation is a very effective method to verify the feasibility of sensor devices for ground observation. The key to remote sensing image application is that simultaneous interpreting of remote sensing images can make use of the different characteristics of different data, eliminate the redundancy and contradiction between different sensors, and improve the timeliness and reliability of remote sensing information extraction. The hotspots and difficulties in this direction are based on remote sensing image simulation of 3D scenes on the ground. Therefore, constructing the 3D scene model on the ground rapidly and accurately is the focus of current research. Because different scenes have different radiation characteristics, therefore, when using MATLAB to write a program generated by 3D scenes, 3D scenes must be saved as different text files according to different scene types, and then extension program of the scene is written to solve the defect that the calculation efficiency is not ideal due to the huge amount of data. This paper uses POV ray photon reverse tracking software to simulate the imaging process of remote sensing sensors, coordinate transformation is used to convert a triangle text file to POV ray readable information and input the RGB value of the base color based on the colorimetry principle, and the final 3D scene is visualized. This paper analyzes the thermal radiation characteristics of the scene and proves the rationality of the scene simulation. The experimental results show that introducing the chroma in the visualization of the scene model makes the whole scene have not only fidelity, but also radiation characteristics in shape and color. This is indispensable in existing 3D modeling and visualization studies. Compared with the complex radiation transmission method, using the multiple angle two-dimensional image generated by POV rays to analyze the radiation characteristics of the scene, the result is intuitive and easy to understand.
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Guo, Xirong, Peng Huang, and Wenyi Zhang. "3D Visualization Management System of Remote Sensing Satellite Data." Procedia Environmental Sciences 10 (2011): 1059–64. http://dx.doi.org/10.1016/j.proenv.2011.09.169.
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Li, Xiao Jing, Tong Pan, Ting Ting Liu, and Hao Peng Wang. "Research on Remote-Sensing Data Syncretizing of Vegetation-Virtual-Reality-Simulation." Applied Mechanics and Materials 336-338 (July 2013): 1426–29. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1426.
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Discussed the key effects and basic principles of data fusion of remote sensing for realizing plants virtual reality simulation. Focused on researching and presenting the general methods of data fusion of remote sensing, application interfaces, and 3D visual display of virtual plants. Through the research, the visual display of visual plants will be realized with full remote sensing interfaces of real-time transferring and intervening.
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Lindberg, Eva, and Johan Holmgren. "Individual Tree Crown Methods for 3D Data from Remote Sensing." Current Forestry Reports 3, no. 1 (February 7, 2017): 19–31. http://dx.doi.org/10.1007/s40725-017-0051-6.
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Zhang, Haiming, Mingchang Wang, Fengyan Wang, Guodong Yang, Ying Zhang, Junqian Jia, and Siqi Wang. "A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data." Remote Sensing 13, no. 3 (January 27, 2021): 440. http://dx.doi.org/10.3390/rs13030440.
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Building Change Detection (BCD) is one of the core issues in earth observation and has received extensive attention in recent years. With the rapid development of earth observation technology, the data source of remote sensing change detection is continuously enriched, which provides the possibility to describe the spatial details of the ground objects more finely and to characterize the ground objects with multiple perspectives and levels. However, due to the different physical mechanisms of multi-source remote sensing data, BCD based on heterogeneous data is a challenge. Previous studies mostly focused on the BCD of homogeneous remote sensing data, while the use of multi-source remote sensing data and considering multiple features to conduct 2D and 3D BCD research is sporadic. In this article, we propose a novel and general squeeze-and-excitation W-Net, which is developed from U-Net and SE-Net. Its unique advantage is that it can not only be used for BCD of homogeneous and heterogeneous remote sensing data respectively but also can input both homogeneous and heterogeneous remote sensing data for 2D or 3D BCD by relying on its bidirectional symmetric end-to-end network architecture. Moreover, from a unique perspective, we use image features that are stable in performance and less affected by radiation differences and temporal changes. We innovatively introduced the squeeze-and-excitation module to explicitly model the interdependence between feature channels so that the response between the feature channels is adaptively recalibrated to improve the information mining ability and detection accuracy of the model. As far as we know, this is the first proposed network architecture that can simultaneously use multi-source and multi-feature remote sensing data for 2D and 3D BCD. The experimental results in two 2D data sets and two challenging 3D data sets demonstrate that the promising performances of the squeeze-and-excitation W-Net outperform several traditional and state-of-the-art approaches. Moreover, both visual and quantitative analyses of the experimental results demonstrate competitive performance in the proposed network. This demonstrates that the proposed network and method are practical, physically justified, and have great potential application value in large-scale 2D and 3D BCD and qualitative and quantitative research.
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Xuhan, Huijun Yang, Qiufeng Shen, Jiangtao Yang, Huihui Liang, Cancan Bao, and Shuang Cang. "Automatic Terrain Debris Recognition Network Based on 3D Remote Sensing Data." Computers, Materials & Continua 65, no. 1 (2020): 579–96. http://dx.doi.org/10.32604/cmc.2020.011262.
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Schröter, Kai, Stefan Lüdtke, Richard Redweik, Jessica Meier, Mathias Bochow, Lutz Ross, Claus Nagel, and Heidi Kreibich. "Flood loss estimation using 3D city models and remote sensing data." Environmental Modelling & Software 105 (July 2018): 118–31. http://dx.doi.org/10.1016/j.envsoft.2018.03.032.
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Latifi, Hooman, and Ruben Valbuena. "Current Trends in Forest Ecological Applications of Three-Dimensional Remote Sensing: Transition from Experimental to Operational Solutions?" Forests 10, no. 10 (October 9, 2019): 891. http://dx.doi.org/10.3390/f10100891.
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The alarming increase in the magnitude and spatiotemporal patterns of changes in composition, structure and function of forest ecosystems during recent years calls for enhanced cross-border mitigation and adaption measures, which strongly entail intensified research to understand the underlying processes in the ecosystems as well as their dynamics. Remote sensing data and methods are nowadays the main complementary sources of synoptic, up-to-date and objective information to support field observations in forest ecology. In particular, analysis of three-dimensional (3D) remote sensing data is regarded as an appropriate complement, since they are hypothesized to resemble the 3D character of most forest attributes. Following their use in various small-scale forest structural analyses over the past two decades, these sources of data are now on their way to be integrated in novel applications in fields like citizen science, environmental impact assessment, forest fire analysis, and biodiversity assessment in remote areas. These and a number of other novel applications provide valuable material for the Forests special issue “3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function”, which shows the promising future of these technologies and improves our understanding of the potentials and challenges of 3D remote sensing in practical forest ecology worldwide.
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Li Xusheng, 栗旭升, 陈冬花 Chen Donghua, 刘赛赛 Liu Saisai, 张乃明 Zhang Naiming, and 李虎 Li Hu. "Tree-Species Identification of Multisource Remote-Sensing Data using Improved 3D-CNN." Laser & Optoelectronics Progress 57, no. 24 (2020): 242804. http://dx.doi.org/10.3788/lop57.242804.
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Truong-Hong, Linh, and Debra F. Laefer. "Quantitative evaluation strategies for urban 3D model generation from remote sensing data." Computers & Graphics 49 (June 2015): 82–91. http://dx.doi.org/10.1016/j.cag.2015.03.001.
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Baracchini, Theo, Philip Y. Chu, Jonas Šukys, Gian Lieberherr, Stefan Wunderle, Alfred Wüest, and Damien Bouffard. "Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4." Geoscientific Model Development 13, no. 3 (March 17, 2020): 1267–84. http://dx.doi.org/10.5194/gmd-13-1267-2020.
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Abstract. The understanding of physical dynamics is crucial to provide scientifically credible information on lake ecosystem management. We show how the combination of in situ observations, remote sensing data, and three-dimensional hydrodynamic (3D) numerical simulations is capable of resolving various spatiotemporal scales involved in lake dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we develop a flexible framework by incorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalman filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in situ and satellite remote sensing temperature data into a 3D hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatiotemporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed with the goal of near-real-time operational systems (e.g., integration into meteolakes.ch).
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Sun, X. F., and X. G. Lin. "RANDOM-FOREST-ENSEMBLE-BASED CLASSIFICATION OF HIGH-RESOLUTION REMOTE SENSING IMAGES AND NDSM OVER URBAN AREAS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 887–92. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-887-2017.
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As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample’s category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data.
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Li, J., J. Sheng, Y. Chen, L. Ke, N. Yao, Z. Miao, X. Zeng, L. Hu, and Q. Wang. "A WEB-BASED LEARNING ENVIRONMENT OF REMOTE SENSING EXPERIMENTAL CLASS WITH PYTHON." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2020 (August 24, 2020): 57–61. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2020-57-2020.
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Abstract. Remote sensing course is a general disciplinary required course of human geography and urban-rural planning major. Its class hour is 48, including theoretical classes and experimental classes. Rapid technological developments is remote sensing area demand quick and steady changes in the education programme and its realization, especially in experimental classes. Experimental classes include: introduction to remote sensing software and basic operations, remote sensing data pre-processing (input, output, 2D and 3D terrain display, image cut, image mosaic, and projection transformation), remote sensing image enhancement, remote sensing image transformation, computer aided classification, image interpretation, and remote sensing image terrain analysis. There are two difficulties in the remote sensing experimental classes. First, it cost a lot of time to prepare the remote sensing software and the remote sensing images. Second, some students just want to use the remote sensing as a tool to investigate environment changing, some other students may want to study more remote sensing image processing technologies. A web-based learning environment of remote sensing is developed to facilitate the application of remote sensing experimental teaching. To make the learning more effective, there are eight modules including four optional modules. The Python programming language is chosen to implement the web-based remote sensing learning environment. The web-based learning environment is implemented in a local network server, including the remote sensing data processing algorithms and many satellite image data. Students can easily exercise the remote sensing experimental courses by connecting to the local network server. It is developed mainly for remote sensing experimental course, and also can be adopted by digital image processing or other courses. The feature of web-based learning may be very useful as the online education adopted because of Corona Virus Disease 2019. The results are encouraging and some recommendations will be extracted for the future.
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Nyan, L. T., A. I. Gavrilov, and M. T. Do. "Classification of Hyperspectral Remote Earth Sensing Data using Combined 3D--2D Convolutional Neural Networks." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 1 (138) (March 2022): 100–118. http://dx.doi.org/10.18698/0236-3933-2022-1-100-118.
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Hyperspectral image classification is used for analyzing remote Earth sensing data. Convolutional neural network is one of the most commonly used methods for processing visual data based on deep learning. The article considers the proposed hybrid 3D--2D spectral convolutional neural network for hyperspectral image classification. At the initial stage, a simple combined trained deep learning model was proposed, which was constructed by combining 2D and 3D convolutional neural networks to extract deeper spatial-spectral features with fewer 3D--2D convolutions. The 3D network facilitates the joint spatial-spectral representation of objects from a stack of spectral bands. Functions of 3D--2D convolutional neural networks were used for classifying hyperspectral images. The algorithm of the method of principal components is applied to reduce the dimension. Hyperspectral image classification experiments were performed on Indian Pines, University of Pavia and Salinas Scene remote sensing datasets. The first layer of the feature map is used as input for subsequent layers in predicting final labels for each hyperspectral pixel. The proposed method not only includes the benefits of advanced feature extraction from convolutional neural networks, but also makes full use of spectral and spatial information. The effectiveness of the proposed method was tested on three reference data sets. The results show that a multifunctional learning system based on such networks significantly improves classification accuracy (more than 99 %)
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Cheng, S., M. Dou, J. Wang, S. Zhang, and X. Chen. "APPROACH TO CONSTRUCTING 3D VIRTUAL SCENE OF IRRIGATION AREA USING MULTI-SOURCE DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2/W2 (October 19, 2015): 227–33. http://dx.doi.org/10.5194/isprsannals-ii-2-w2-227-2015.
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For an irrigation area that is often complicated by various 3D artificial ground features and natural environment, disadvantages of traditional 2D GIS in spatial data representation, management, query, analysis and visualization is becoming more and more evident. Building a more realistic 3D virtual scene is thus especially urgent for irrigation area managers and decision makers, so that they can carry out various irrigational operations lively and intuitively. Based on previous researchers' achievements, a simple, practical and cost-effective approach was proposed in this study, by adopting3D geographic information system (3D GIS), remote sensing (RS) technology. Based on multi-source data such as Google Earth (GE) high-resolution remote sensing image, ASTER G-DEM, hydrological facility maps and so on, 3D terrain model and ground feature models were created interactively. Both of the models were then rendered with texture data and integrated under ArcGIS platform. A vivid, realistic 3D virtual scene of irrigation area that has a good visual effect and possesses primary GIS functions about data query and analysis was constructed.Yet, there is still a long way to go for establishing a true 3D GIS for the irrigation are: issues of this study were deeply discussed and future research direction was pointed out in the end of the paper.
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Su, Hua, Jinwen Jiang, An Wang, Wei Zhuang, and Xiao-Hai Yan. "Subsurface Temperature Reconstruction for the Global Ocean from 1993 to 2020 Using Satellite Observations and Deep Learning." Remote Sensing 14, no. 13 (July 3, 2022): 3198. http://dx.doi.org/10.3390/rs14133198.
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The reconstruction of the ocean’s 3D thermal structure is essential to the study of ocean interior processes and global climate change. Satellite remote sensing technology can collect large-scale, high-resolution ocean observation data, but only at the surface layer. Based on empirical statistical and artificial intelligence models, deep ocean remote sensing techniques allow us to retrieve and reconstruct the 3D ocean temperature structure by combining surface remote sensing observations with in situ float observations. This study proposed a new deep learning method, Convolutional Long Short-Term Memory (ConvLSTM) neural networks, which combines multisource remote sensing observations and Argo gridded data to reconstruct and produce a new long-time-series global ocean subsurface temperature (ST) dataset for the upper 2000 m from 1993 to 2020, which is named the Deep Ocean Remote Sensing (DORS) product. The data-driven ConvLSTM model can learn the spatiotemporal features of ocean observation data, significantly improves the model’s robustness and generalization ability, and outperforms the LighGBM model for the data reconstruction. The validation results show our DORS dataset has high accuracy with an average R2 and RMSE of 0.99/0.34 °C compared to the Argo gridded dataset, and the average R2 and NRMSE validated by the EN4-Profile dataset over the time series are 0.94/0.05 °C. Furthermore, the ST structure between DORS and Argo has good consistency in the 3D spatial morphology and distribution pattern, indicating that the DORS dataset has high quality and strong reliability, and well fills the pre-Argo data gaps. We effectively track the global ocean warming in the upper 2000 m from 1993 to 2020 based on the DORS dataset, and we further examine and understand the spatial patterns, evolution trends, and vertical characteristics of global ST changes. From 1993 to 2020, the average global ocean temperature warming trend is 0.063 °C/decade for the upper 2000 m. The 3D temperature trends revealed significant spatial heterogeneity across different ocean basins. Since 2005, the warming signal has become more significant in the subsurface and deeper ocean. From a remote sensing standpoint, the DORS product can provide new and robust data support for ocean interior process and climate change studies.
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Xu, Bo, Mo Wen Xie, and Li Wei Wang. "Preliminary Analysis Method of Reservoir Leakage Based on GIS." Advanced Materials Research 838-841 (November 2013): 1641–50. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.1641.
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Due to the geological complexity of the wide reservoir area, reservoir leakage problem has been the subject of Research in Water Resources and Hydropower Engineering. In recent years, the development and application of GIS (Geographic Information System), remote sensing technology and three-dimensional (3D) technology, have provided a powerful tool in analysis of the reservoir leakage problem. Based on the 3D remote sensing image visualization system created with remote sensing technology, GIS, 3D technology, this paper studies the leakage problem of a reservoir. By analyzing the terrain data, we can find the might existing leaking channels combining, combining lithology, geological structure and hydrogeological conditions. Then calculate the leakage quantity to evaluate the reservoir leakage. With the characteristic of accuracy and timeliness, the system will play an important role in preliminary analysis of reservoir leakage problemas well as forecasting decision making.
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Mitsevich, L. "3D AERODROME OBSTACLE ASSESSMENT USING STEREO REMOTE SENSING IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 14, 2020): 1115–19. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1115-2020.
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Abstract. The paper reveals the photogrammetric methods of the aerodrome obstacle data collection and assessment in accordance with the International Civil Aviation Organization (ICAO) requirements. High artificial or natural vertical objects (obstacles) near a runway can cause accidents during aircraft take-off and landing. There are a series of limitation surfaces defining allowable obstacle heights. Identification and assessment of obstacles extending above the limitation surfaces are important steps for aerodrome certification. To this end, the periodic aerodrome obstacle survey procedure is implemented mostly by ground geodetic methods. The goal of the research was to develop the technology for remote and effective obstacle identification and assessment processes using remote sensing stereo imagery. The photogrammetric methods were based on the three-dimensional vector models that were integrated into the stereo pair of satellite and aviation scanner images. The obstacle extends above the limitation surfaces evaluated in a semi-automatic mode, mathematically and visually controlled. The advantages of the stereo photogrammetric methods are discussed. The ecological aspects of precise evaluation of forested areas as critical obstacles considered. Examples of the implementation of this technology for aerodromes of the Republic of Belarus are given.
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Laukamp, Carsten, Maarten Haest, and Thomas Cudahy. "The Rocklea Dome 3D Mineral Mapping Test Data Set." Earth System Science Data 13, no. 3 (March 30, 2021): 1371–83. http://dx.doi.org/10.5194/essd-13-1371-2021.
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Abstract. The integration of surface and subsurface geoscience data is critical for efficient and effective mineral exploration and mining. Publicly accessible data sets to evaluate the various geoscience analytical tools and their effectiveness for characterisation of mineral assemblages and lithologies or discrimination of ore from waste are however scarce. The open-access Rocklea Dome 3D Mineral Mapping Test Data Set (Laukamp, 2020; https://doi.org/10.25919/5ed83bf55be6a) provides an opportunity for evaluating proximal and remote sensing data, validated and calibrated by independent geochemical and mineralogical analyses, for exploration of channel iron deposits (CIDs) through cover. We present hyperspectral airborne, surface, and drill core reflectance spectra collected in the visible–near-infrared and shortwave infrared wavelength ranges (VNIR–SWIR; 350 to 2500 nm), as well as whole-rock geochemistry obtained by means of X-ray fluorescence analysis and loss-on-ignition measurements of drill core samples. The integration of surface with subsurface hyperspectral data collected in the frame of previously published Rocklea Dome 3D Mineral Mapping case studies demonstrated that about 30 % of exploration drill holes were sunk into barren ground and could have been of better use, located elsewhere, if airborne hyperspectral imagery had been consulted for drill hole planning. The remote mapping of transported Tertiary detritals (i.e. potential hosts of channel iron ore resources) versus weathered in situ Archaean bedrock (i.e. barren ground) has significant implications for other areas where “cover” (i.e. regolith and/or sediments covering bedrock hosting mineral deposits) hinders mineral exploration. Hyperspectral remote sensing represents a cost-effective method for regolith landform mapping required for planning drilling programmes. In the Rocklea Dome area, vegetation unmixing methods applied to airborne hyperspectral data, integrated with subsurface data, resulted in seamless mapping of ore zones from the weathered surface to the base of the CID – a concept that can be applied to other mineral exploration and mineral deposit studies. Furthermore, the associated, independent calibration data allowed the quantification of iron oxide phases and associated mineralogy from hyperspectral data. Using the Rocklea Dome data set, novel geostatistical clustering methods were applied to the drill core data sets for ore body domaining that introduced scientific rigour to a traditionally subjective procedure, resulting in reproducible objective domains that are critical for the mining process. Beyond the previously published case studies, the Rocklea Dome 3D Mineral Mapping Test Data Set has the potential to develop new methods for advanced resource characterisation and develop new applications that aid exploration for mineral deposits through cover. The white mica and chlorite abundance maps derived from airborne hyperspectral, presented here for the first time, highlight the additional applications of remote sensing for geological mapping and could help to evaluate newly launched hyper- and multispectral spaceborne systems for geoscience and mineral exploration.
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Partha, C. G. I., I. N. Budiastra, A. A. N. Amrita, and I. M. Suartika. "Remote Sensing Systems At The Rocket's Payload Test." Journal of Electrical, Electronics and Informatics 2, no. 2 (December 18, 2018): 44. http://dx.doi.org/10.24843/jeei.2018.v02.i02.p05.
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Aviation technology and space is one of the leading technology for developed countries, especially in the form of rocket technology and payload. Countries that are capable of mastering these technologies will be respected by countries all over the world. Indonesia as the island nation and the State's large and extensive maritime already should have independence in the mastery of the technology of the rocket and payload. Therefore, continuous efforts are required to achieve independence, including through enhancing aviation technology and space technology, particularly at early stages the rocket and payload. Remote Sensing Systems At the rocket's Payload was Test remote monitoring system image capture and the attitude of the launch payload through the computer screen (display) continuously (real-time) data obtained from sensors that are mounted on the rocket's payload. 3D point (x, y, z) must be expressed as a graph visualization perspective drawings of rockets with the appropriate direction. The radar conducted computer GS (Ground Segment) or Ground Control Station (GCS). The result of the attitude of the Rocket Test launch Payloads have been able to do the communication data transmission of images and data for 3D (x, y, and z) in real-time to the Ground segment. Wireless communication uses radio telemetry frequency 433 MHz, power of 100 mW, the distance range obtained in this study a maximum of 1000 meter in conditions in the air and without obstruction.
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Juhász, Attila, and Hajnalka Neuberger. "Detecting Military Historical Objects by LiDAR Data." Academic and Applied Research in Military and Public Management Science 14, no. 2 (June 30, 2015): 219–36. http://dx.doi.org/10.32565/aarms.2015.2.8.
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Light Detection and Ranging (LiDAR) technology has become one of the major remote sensing methods in the last few years. There are several areas, where the scanned 3D point-clouds can be used very efficiently. In our study we review the potential applications of LiDAR data in military historical reconstruction. Previously we defined the major steps of the entire reconstruction process and the – mostly archive – useful data sources. Obviously the base of this kind of investigations must be archive data, but it is an interesting challenge to integrate a cutting edge method into such tasks. LiDAR technology can be very useful, especially in vegetation covered areas, where the conventional remote sensing technologies are mostly inefficient. We shall summarize how laser scanned data can support the different parts of reconstruction work and define the technological steps of LiDAR data processing.
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Cibula, Róbert, and Ivan Sačkov. "An integrated framework for Web-based visualisation of forest resources estimated from remote sensing data." Central European Forestry Journal 66, no. 3 (August 24, 2020): 170–76. http://dx.doi.org/10.2478/forj-2020-0004.
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Abstract Advanced remote sensing technologies has recently become an effective tool for monitoring of forest ecosystems. However, there is a growing need for online dissemination of geospatial data from these activities. We developed and assessed a framework which integrates (1) an algorithm for estimation of forest stand variables based on remote sensing data and (2) a web-map application for 2D and 3D visualisation of geospatial data. The performance of proposed framework was assessed in a Forest Management Unit Vígľaš (Slovakia, Central Europe) covering a total area of 12,472 ha. The mean error of remote sensing-based estimations of forest resources reached values of 16.4%, 12.1%, –26.8%, and –35.4% for the mean height, mean diameter, volume per hectare, and trees per hectare, respectively. The web-map application is stable and allows real-time visualization of digital terrain model, aerial imagery, thematic maps used in forestry or geology, and 968,217 single trees at forest management unit level.
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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.
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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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Diab, Ahmed, Rasha Kashef, and Ahmed Shaker. "Deep Learning for LiDAR Point Cloud Classification in Remote Sensing." Sensors 22, no. 20 (October 16, 2022): 7868. http://dx.doi.org/10.3390/s22207868.
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Point clouds are one of the most widely used data formats produced by depth sensors. There is a lot of research into feature extraction from unordered and irregular point cloud data. Deep learning in computer vision achieves great performance for data classification and segmentation of 3D data points as point clouds. Various research has been conducted on point clouds and remote sensing tasks using deep learning (DL) methods. However, there is a research gap in providing a road map of existing work, including limitations and challenges. This paper focuses on introducing the state-of-the-art DL models, categorized by the structure of the data they consume. The models’ performance is collected, and results are provided for benchmarking on the most used datasets. Additionally, we summarize the current benchmark 3D datasets publicly available for DL training and testing. In our comparative study, we can conclude that convolutional neural networks (CNNs) achieve the best performance in various remote-sensing applications while being light-weighted models, namely Dynamic Graph CNN (DGCNN) and ConvPoint.
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Han, Yanling, Cong Wei, Ruyan Zhou, Zhonghua Hong, Yun Zhang, and Shuhu Yang. "Combining 3D-CNN and Squeeze-and-Excitation Networks for Remote Sensing Sea Ice Image Classification." Mathematical Problems in Engineering 2020 (April 7, 2020): 1–15. http://dx.doi.org/10.1155/2020/8065396.
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Sea ice is one of the most prominent marine disasters in high latitudes. Remote sensing technology provides an effective means for sea ice detection. Remote sensing sea ice images contain rich spectral and spatial information. However, most traditional methods only focus on spectral information or spatial information, and do not excavate the feature of spectral and spatial simultaneously in remote sensing sea ice images classification. At the same time, the complex correlation characteristics among spectra and small sample problem in sea ice classification also limit the improvement of sea ice classification accuracy. For this issue, this paper proposes a new remote sensing sea ice image classification method based on squeeze-and-excitation (SE) network, convolutional neural network (CNN), and support vector machines (SVMs). The proposed method designs 3D-CNN deep network so as to fully exploit the spatial-spectrum features of remote sensing sea ice images and integrates SE-Block into 3D-CNN in-depth network in order to distinguish the contributions of different spectra to sea ice classification. According to the different contributions of spectral features, the weight of each spectral feature is optimized by fusing SE-Block in order to further enhance the sample quality. Finally, information-rich and representative samples are chosen by combining the idea of active learning and input into SVM classifier, and this achieves superior classification accuracy of remote sensing sea ice images with small samples. In order to verify the effectiveness of the proposed method, we conducted experiments on three different data from Baffin Bay, Bohai Bay, and Liaodong Bay. The experimental results show that compared with other classical classification methods, the proposed method comprehensively considers the correlation among spectral features and the small samples problems and deeply excavates the spatial-spectrum characteristics of sea ice and achieves better classification performance, which can be effectively applied to remote sensing sea ice image classification.
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Molebny, V. V., G. W. Kamerman, and O. Steinvall. "Laser remote sensing: yesterday, today and tomorrow." Electronics and Communications 16, no. 3 (March 28, 2011): 68–73. http://dx.doi.org/10.20535/2312-1807.2011.16.3.265061.
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Development of laser remote sensing (lidar echniques) is discussed for atmosphere and ocean investigation. Examples of Raman lidars based on vibrational and rotational energy states of molecular species are demonstrated for remote detection and monitoring of pollution, as well as for studies of dynamics of different components and their parameters. Coherent laser radars allowed remote measurement of speed and vibrations, Doppler velocity information being crucial for the solution of the problem of windshear detection and flight security. A promising trend in laser radar development is incorporation of range and velocity data into the image information. Gated imaging, as one of the 3D techniques, demonstrated its prospects (looking through scattering media, vegetation, dress, etc.) for military and civilian use.
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Delacourt, Christophe, Pascal Allemand, Etienne Berthier, Daniel Raucoules, Bérangère Casson, Philippe Grandjean, Claude Pambrun, and Eric Varel. "Remote-sensing techniques for analysing landslide kinematics: a review." Bulletin de la Société Géologique de France 178, no. 2 (March 1, 2007): 89–100. http://dx.doi.org/10.2113/gssgfbull.178.2.89.
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Abstract Surface displacement field of landslides is a key parameter to access to their geometries and mechanical properties. Surface displacements can be calculated using remote-sensing methods such as interferometry for radar data and image correlation for optical data. These methods have been elaborated this last decade and successfully applied on sensors (radar, cameras, terrestrial 3D laser scanner imaging) either attached to space or aerial platforms such as satellites, planes, and unmanned radio-controlled platforms (drones and helicopters) or settled at fixed positions emplaced in the front of landslides. This paper reviews the techniques of image analysis (interferometry and optical data correlation) to measure displacements and examines the performance of each type of platforms. Examples of applications of these techniques in French South Alps are shown. Depending on the landslide characteristics (exposure conditions, size, velocity) as well as the goal of the study (operational or scientific purpose), one or a combination of several techniques and data (characterized by several resolution, accuracy, covered surface, revisiting time) have to be used. Radar satellite data processed with differential interferometric or PS methods are mainly suitable for scientific purposes due to various application limitations in mountainous area. Optical satellite and aerial images can be used for scientific studies at fairly high resolution but are strongly dependant on atmospheric conditions. Platforms and sensors such as drone, fixed camera, fixed radar and Lidar have the advantage of high adaptability. They can be used to obtain very high resolution and precise 3D data (of centimetric accuracy) suitable for both scientific and operational purposes.
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Honkavaara, E., T. Hakala, O. Nevalainen, N. Viljanen, T. Rosnell, E. Khoramshahi, R. Näsi, R. Oliveira, and A. Tommaselli. "GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 77–82. http://dx.doi.org/10.5194/isprs-archives-xli-b7-77-2016.
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Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV) and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.
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Honkavaara, E., T. Hakala, O. Nevalainen, N. Viljanen, T. Rosnell, E. Khoramshahi, R. Näsi, R. Oliveira, and A. Tommaselli. "GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 77–82. http://dx.doi.org/10.5194/isprsarchives-xli-b7-77-2016.
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Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV) and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.
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Hamana, Masahiro, and Teruhisa Komatsu. "Mapping 3D structure of a Sargassum forest with high-resolution sounding data obtained by multibeam echosounder." ICES Journal of Marine Science 78, no. 4 (March 13, 2021): 1458–69. http://dx.doi.org/10.1093/icesjms/fsab044.
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Abstract Sargassum forests play an important role in coastal waters as habitats for marine organisms, including commercial species. However, human activities have negatively affected their distribution causing a worldwide decline of Sargassum forests. Mapping and monitoring the distribution and biomass of these habitats using acoustic remote sensing techniques is key for their conservation. Nonetheless, most researches based on acoustic remote sensing methods focus on estimations of macrophyte area and its canopy height, and less researches reporting 3D visualization of these habitats. This study demonstrates the use of high-resolution multibeam echosounder (MBES) bathymetric data to visualize the 3D structure of Sargassum forests. Comparing acoustic data and underwater camera photos collected in field surveys, we identified Sargassum individuals as vertical clusters of contiguous sounding points with a base close to the sea bottom in the sounding data of the MBES. Using this criterion, we could distinguish Sargassum echoes, visualize the 3D structure of Sargassum forests and estimate the number of Sargassum individuals in the survey area. Using the relation between thallus length and dry weight of sampled Sargassum plants, standing stock and biomass could be estimated assuming the thallus length was the height of Sargassum plants identified with the MBES.
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Zargar, Er Tajamul. "The Revolutionary Role of Remote Sensing in Civil Engineering." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1791–96. http://dx.doi.org/10.22214/ijraset.2021.39109.
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Abstract: Civil engineering is considered as the second oldest engineering discipline of the world. It deals with the design, maintenance and constructions of different structural and building elements like roads, bridges, dams etc. It comprises of many sub divisions like surveying, water resources, environment etc. Remote sensing plays a key role in acquiring and providing topographical data and 3D images. It also helps in examining existing structures and layouts. Thus remote sensing is indispensable in the field of civil engineering. This paper tries to give a brief overview of what remote sensing is and how it plays a vital role in making civil engineering more convenient, simple and efficient.
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Pan, Jianping, Xin Li, Zhuoyan Cai, Bowen Sun, and Wei Cui. "A Self-Attentive Hybrid Coding Network for 3D Change Detection in High-Resolution Optical Stereo Images." Remote Sensing 14, no. 9 (April 25, 2022): 2046. http://dx.doi.org/10.3390/rs14092046.
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Real-time monitoring of urban building development provides a basis for urban planning and management. Remote sensing change detection is a key technology for achieving this goal. Intelligent change detection based on deep learning of remote sensing images is a current focus of research. However, most methods only use unimodal remote sensing data and ignore vertical features, leading to incomplete characterization, poor detection of small targets, and false detections and omissions. To solve these problems, we propose a multi-path self-attentive hybrid coding network model (MAHNet) that fuses high-resolution remote sensing images and digital surface models (DSMs) for 3D change detection of urban buildings. We use stereo images from the Gaofen-7 (GF-7) stereo mapping satellite as the data source. In the encoding stage, we propose a multi-path hybrid encoder, which is a structure that can efficiently perform multi-dimensional feature mining of multimodal data. In the deep feature fusion link, a dual self-attentive fusion structure is designed that can improve the deep feature fusion and characterization of multimodal data. In the decoding stage, a dense skip-connection decoder is designed that can fuse multi-scale features flexibly and reduce spatial information losses in small-change regions in the down-sampling process, while enhancing feature utilization and propagation efficiency. Experimental results show that MAHNet achieves accurate pixel-level change detection in complex urban scenes with an overall accuracy of 97.44% and F1-score of 92.59%, thereby outperforming other methods of change detection.
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Pan, Jianping, Xin Li, Zhuoyan Cai, Bowen Sun, and Wei Cui. "A Self-Attentive Hybrid Coding Network for 3D Change Detection in High-Resolution Optical Stereo Images." Remote Sensing 14, no. 9 (April 25, 2022): 2046. http://dx.doi.org/10.3390/rs14092046.
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Real-time monitoring of urban building development provides a basis for urban planning and management. Remote sensing change detection is a key technology for achieving this goal. Intelligent change detection based on deep learning of remote sensing images is a current focus of research. However, most methods only use unimodal remote sensing data and ignore vertical features, leading to incomplete characterization, poor detection of small targets, and false detections and omissions. To solve these problems, we propose a multi-path self-attentive hybrid coding network model (MAHNet) that fuses high-resolution remote sensing images and digital surface models (DSMs) for 3D change detection of urban buildings. We use stereo images from the Gaofen-7 (GF-7) stereo mapping satellite as the data source. In the encoding stage, we propose a multi-path hybrid encoder, which is a structure that can efficiently perform multi-dimensional feature mining of multimodal data. In the deep feature fusion link, a dual self-attentive fusion structure is designed that can improve the deep feature fusion and characterization of multimodal data. In the decoding stage, a dense skip-connection decoder is designed that can fuse multi-scale features flexibly and reduce spatial information losses in small-change regions in the down-sampling process, while enhancing feature utilization and propagation efficiency. Experimental results show that MAHNet achieves accurate pixel-level change detection in complex urban scenes with an overall accuracy of 97.44% and F1-score of 92.59%, thereby outperforming other methods of change detection.
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WANG, LI, YUXI WU, JIPING XU, HUIYAN ZHANG, XIAOYI WANG, JIABIN YU, QIAN SUN, and ZHIYAO ZHAO. "STATUS PREDICTION BY 3D FRACTAL NET CNN BASED ON REMOTE SENSING IMAGES." Fractals 28, no. 08 (July 10, 2020): 2040018. http://dx.doi.org/10.1142/s0218348x20400186.
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The contradiction between the supply and demand of water resources is becoming increasingly prominent, whose main reason is the eutrophication of rivers and lakes. However, limited and inaccurate data makes it impossible to establish a precise model to successfully predict eutrophication levels. Moreover, it is incompetent to distinguish the degree of eutrophication status of lakes by manual calculation and processing. Focusing on these inconveniences, this study proposes 3D fractal net CNN to extract features in remote sensing images automatically, aiming at achieving scientific forecasting on eutrophication status of lakes. In order to certificate the effectiveness of the proposed method, we predict the state of the water body based on remote sensing images of natural lake. The images in natural lake were accessed by MODIS satellite, cloud-free chlorophyll inversion picture of 2009 was resized into [Formula: see text] patches, which were collected as training and testing samples. In the total of 162 pictures, our study makes three consecutive pictures as a set of data so as to attain 120 group of training and 40 testing data. Taking one set of data as input of the neural network and the next day’s eutrophication level as labels, CNNs act considerable efficiency. Through the experimental results of 2D CNN, 3D CNN and 3D fractal net CNN, 3D fractal net CNN has more outstanding performance than the other two, with the prediction accuracy of 67.5% better than 47.5% and 62.5%, respectively.
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Juhász, A., and H. Neuberger. "REMOTELY SENSED DATA FUSION IN MODERN AGE ARCHAEOLOGY AND MILITARY HISTORICAL RECONSTRUCTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 281–86. http://dx.doi.org/10.5194/isprs-archives-xli-b5-281-2016.
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LiDAR technology has become one of the major remote sensing methods in the last few years. There are several areas, where the scanned 3D point clouds can be used very efficiently. In our study we review the potential applications of LiDAR data in military historical reconstruction. Obviously, the base of this kind of investigation must be the archive data, but it is an interesting challenge to integrate a cutting edge method into such tasks. The LiDAR technology can be very useful, especially in vegetation covered areas, where the conventional remote sensing technologies are mostly inefficient. We review two typical sample projects where we integrated LiDAR data in military historical GIS reconstruction. Finally, we summarize, how laser scanned data can support the different parts of reconstruction work and define the technological steps of LiDAR data processing.
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Juhász, A., and H. Neuberger. "REMOTELY SENSED DATA FUSION IN MODERN AGE ARCHAEOLOGY AND MILITARY HISTORICAL RECONSTRUCTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 15, 2016): 281–86. http://dx.doi.org/10.5194/isprsarchives-xli-b5-281-2016.
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LiDAR technology has become one of the major remote sensing methods in the last few years. There are several areas, where the scanned 3D point clouds can be used very efficiently. In our study we review the potential applications of LiDAR data in military historical reconstruction. Obviously, the base of this kind of investigation must be the archive data, but it is an interesting challenge to integrate a cutting edge method into such tasks. The LiDAR technology can be very useful, especially in vegetation covered areas, where the conventional remote sensing technologies are mostly inefficient. We review two typical sample projects where we integrated LiDAR data in military historical GIS reconstruction. Finally, we summarize, how laser scanned data can support the different parts of reconstruction work and define the technological steps of LiDAR data processing.
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Drobež, Petra, Dejan Grigillo, Anka Lisec, and Mojca Kosmatin Fras. "Remote sensing data as a potential source for establishment of the 3D cadastre in Slovenia." Geodetski vestnik 60, no. 03 (2016): 392–422. http://dx.doi.org/10.15292/geodetski-vestnik.2016.03.392-422.
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Bennett, Rohan, Peter van Oosterom, Christiaan Lemmen, and Mila Koeva. "Remote Sensing for Land Administration." Remote Sensing 12, no. 15 (August 4, 2020): 2497. http://dx.doi.org/10.3390/rs12152497.
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Land administration constitutes the socio-technical systems that govern land tenure, use, value and development within a jurisdiction. The land parcel is the fundamental unit of analysis. Each parcel has identifiable boundaries, associated rights, and linked parties. Spatial information is fundamental. It represents the boundaries between land parcels and is embedded in cadastral sketches, plans, maps and databases. The boundaries are expressed in these records using mathematical or graphical descriptions. They are also expressed physically with monuments or natural features. Ideally, the recorded and physical expressions should align, however, in practice, this may not occur. This means some boundaries may be physically invisible, lacking accurate documentation, or potentially both. Emerging remote sensing tools and techniques offers great potential. Historically, the measurements used to produce recorded boundary representations were generated from ground-based surveying techniques. The approach was, and remains, entirely appropriate in many circumstances, although it can be timely, costly, and may only capture very limited contextual boundary information. Meanwhile, advances in remote sensing and photogrammetry offer improved measurement speeds, reduced costs, higher image resolutions, and enhanced sampling granularity. Applications of unmanned aerial vehicles (UAV), laser scanning, both airborne and terrestrial (LiDAR), radar interferometry, machine learning, and artificial intelligence techniques, all provide examples. Coupled with emergent societal challenges relating to poverty reduction, rapid urbanisation, vertical development, and complex infrastructure management, the contemporary motivation to use these new techniques is high. Fundamentally, they enable more rapid, cost-effective, and tailored approaches to 2D and 3D land data creation, analysis, and maintenance. This Special Issue hosts papers focusing on this intersection of emergent remote sensing tools and techniques, applied to domain of land administration.
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Luo, Yawen, and Yuhua Chen. "Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet." Sensors 21, no. 8 (April 8, 2021): 2622. http://dx.doi.org/10.3390/s21082622.
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Unmanned Aerial Vehicles (UAVs, also known as drones) have become increasingly appealing with various applications and services over the past years. Drone-based remote sensing has shown its unique advantages in collecting ground-truth and real-time data due to their affordable costs and relative ease of operability. This paper presents a 3D placement scheme for multi-drone sensing/monitoring platforms, where a fleet of drones are sent for conducting a mission in a given area. It can range from environmental monitoring of forestry, survivors searching in a disaster zone to exploring remote regions such as deserts and mountains. The proposed drone placing algorithm covers the entire region without dead zones while minimizing the number of cooperating drones deployed. Naturally, drones have limited battery supplies which need to cover mechanical motions, message transmissions and data calculation. Consequently, the drone energy model is explicitly investigated and dynamic adjustments are deployed on drone locations. The proposed drone placement algorithm is 3D landscaping-aware and it takes the line-of-sight into account. The energy model considers inter-communications within drones. The algorithm not only minimizes the overall energy consumption, but also maximizes the whole drone team’s lifetime in situations where no power recharging facilities are available in remote/rural areas. Simulations show the proposed placement scheme has significantly prolonged the lifetime of the drone fleet with the least number of drones deployed under various complex terrains.
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Gao, Lipeng, Wenzhong Shi, Jun Zhu, Pan Shao, Sitong Sun, Yuanyang Li, Fei Wang, and Fukuan Gao. "Novel Framework for 3D Road Extraction Based on Airborne LiDAR and High-Resolution Remote Sensing Imagery." Remote Sensing 13, no. 23 (November 24, 2021): 4766. http://dx.doi.org/10.3390/rs13234766.
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3D GIS has attracted increasing attention from academics, industries, and governments with the increase in the requirements for the interoperability and integration of different sources of spatial data. Three-dimensional road extraction based on multisource remote sensing data is still a challenging task due to road occlusion and topological complexity. This paper presents a novel framework for 3D road extraction by integrating LiDAR point clouds and high-resolution remote sensing imagery. First, a multiscale collaborative representation-based road probability estimation method was proposed to segment road surfaces from high-resolution remote sensing imagery. Then, an automatic stratification process was conducted to specify the layer values of each road segment. Additionally, a multifactor filtering strategy was proposed in consideration of the complexity of ground features and the existence of noise in LiDAR points. Lastly, a least-square-based elevation interpolation method is used for restoring the elevation information of road sections blocked by overpasses. The experimental results based on two datasets in Hong Kong Island show that the proposed method obtains competitively satisfactory results.
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Jamali, Ali, and Masoud Mahdianpari. "Swin Transformer and Deep Convolutional Neural Networks for Coastal Wetland Classification Using Sentinel-1, Sentinel-2, and LiDAR Data." Remote Sensing 14, no. 2 (January 13, 2022): 359. http://dx.doi.org/10.3390/rs14020359.
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The use of machine learning algorithms to classify complex landscapes has been revolutionized by the introduction of deep learning techniques, particularly in remote sensing. Convolutional neural networks (CNNs) have shown great success in the classification of complex high-dimensional remote sensing imagery, specifically in wetland classification. On the other hand, the state-of-the-art natural language processing (NLP) algorithms are transformers. Although the transformers have been studied for a few remote sensing applications, the integration of deep CNNs and transformers has not been studied, particularly in wetland mapping. As such, in this study, we explore the potential and possible limitations to be overcome regarding the use of a multi-model deep learning network with the integration of a modified version of the well-known deep CNN network of VGG-16, a 3D CNN network, and Swin transformer for complex coastal wetland classification. Moreover, we discuss the potential and limitation of the proposed multi-model technique over several solo models, including a random forest (RF), support vector machine (SVM), VGG-16, 3D CNN, and Swin transformer in the pilot site of Saint John city located in New Brunswick, Canada. In terms of F-1 score, the multi-model network obtained values of 0.87, 0.88, 0.89, 0.91, 0.93, 0.93, and 0.93 for the recognition of shrub wetland, fen, bog, aquatic bed, coastal marsh, forested wetland, and freshwater marsh, respectively. The results suggest that the multi-model network is superior to other solo classifiers from 3.36% to 33.35% in terms of average accuracy. Results achieved in this study suggest the high potential for integrating and using CNN networks with the cutting-edge transformers for the classification of complex landscapes in remote sensing.
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Calders, Kim, Inge Jonckheere, Joanne Nightingale, and Mikko Vastaranta. "Remote Sensing Technology Applications in Forestry and REDD+." Forests 11, no. 2 (February 7, 2020): 188. http://dx.doi.org/10.3390/f11020188.
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Advances in close-range and remote sensing technologies drive innovations in forest resource assessments and monitoring at varying scales. Data acquired with airborne and spaceborne platforms provide us with higher spatial resolution, more frequent coverage and increased spectral information. Recent developments in ground-based sensors have advanced three dimensional (3D) measurements, low-cost permanent systems and community-based monitoring of forests. The REDD+ mechanism has moved the remote sensing community in advancing and developing forest geospatial products which can be used by countries for the international reporting and national forest monitoring. However, there still is an urgent need to better understand the options and limitations of remote and close-range sensing techniques in the field of degradation and forest change assessment. This Special Issue contains 12 studies that provided insight into new advances in the field of remote sensing for forest management and REDD+. This includes developments into algorithm development using satellite data; synthetic aperture radar (SAR); airborne and terrestrial LiDAR; as well as forest reference emissions level (FREL) frameworks.
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Qi, Jianbo, Donghui Xie, Tiangang Yin, Guangjian Yan, Jean-Philippe Gastellu-Etchegorry, Linyuan Li, Wuming Zhang, Xihan Mu, and Leslie K. Norford. "LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes." Remote Sensing of Environment 221 (February 2019): 695–706. http://dx.doi.org/10.1016/j.rse.2018.11.036.
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Schreiner, Simon, Dubravko Culibrk, Michele Bandecchi, Wolfgang Gross, and Wolfgang Middelmann. "Soil monitoring for precision farming using hyperspectral remote sensing and soil sensors." at - Automatisierungstechnik 69, no. 4 (April 1, 2021): 325–35. http://dx.doi.org/10.1515/auto-2020-0042.
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Abstract This work describes an approach to calculate pedological parameter maps using hyperspectral remote sensing and soil sensors. These maps serve as information basis for automated and precise agricultural treatments by tractors and field robots. Soil samples are recorded by a handheld hyperspectral sensor and analyzed in the laboratory for pedological parameters. The transfer of the correlation between these two data sets to aerial hyperspectral images leads to 2D-parameter maps of the soil surface. Additionally, rod-like soil sensors provide local 3D-information of pedological parameters under the soil surface. The goal is to combine the area-covering 2D-parameter maps with the local 3D-information to extrapolate large-scale 3D-parameter maps using AI approaches.
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Meng, Yan, Shanxiong Chen, Yuxuan Liu, Li Li, Zemin Zhang, Tao Ke, and Xiangyun Hu. "Unsupervised Building Extraction from Multimodal Aerial Data Based on Accurate Vegetation Removal and Image Feature Consistency Constraint." Remote Sensing 14, no. 8 (April 15, 2022): 1912. http://dx.doi.org/10.3390/rs14081912.
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Accurate building extraction from remotely sensed data is difficult to perform automatically because of the complex environments and the complex shapes, colours and textures of buildings. Supervised deep-learning-based methods offer a possible solution to solve this problem. However, these methods generally require many high-quality, manually labelled samples to obtain satisfactory test results, and their production is time and labour intensive. For multimodal data with sufficient information, extracting buildings accurately in as unsupervised a manner as possible. Combining remote sensing images and LiDAR point clouds for unsupervised building extraction is not a new idea, but existing methods often experience two problems: (1) the accuracy of vegetation detection is often not high, which leads to limited building extraction accuracy, and (2) they lack a proper mechanism to further refine the building masks. We propose two methods to address these problems, combining aerial images and aerial LiDAR point clouds. First, we improve two recently developed vegetation detection methods to generate accurate initial building masks. We then refine the building masks based on the image feature consistency constraint, which can replace inaccurate LiDAR-derived boundaries with accurate image-based boundaries, remove the remaining vegetation points and recover some missing building points. Our methods do not require manual parameter tuning or manual data labelling, but still exhibit a competitive performance compared to 29 methods: our methods exhibit accuracies higher than or comparable to 19 state-of-the-art methods (including 8 deep-learning-based methods and 11 unsupervised methods, and 9 of them combine remote sensing images and 3D data), and outperform the top 10 methods (4 of them combine remote sensing images and LiDAR data) evaluated using all three test areas of the Vaihingen dataset on the official website of the ISPRS Test Project on Urban Classification and 3D Building Reconstruction in average area quality. These comparative results verify that our unsupervised methods combining multisource data are very effective.
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Zhang, Junzhe, Wei Guo, Bo Zhou, and Gregory S. Okin. "Drone-Based Remote Sensing for Research on Wind Erosion in Drylands: Possible Applications." Remote Sensing 13, no. 2 (January 15, 2021): 283. http://dx.doi.org/10.3390/rs13020283.
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With rapid innovations in drone, camera, and 3D photogrammetry, drone-based remote sensing can accurately and efficiently provide ultra-high resolution imagery and digital surface model (DSM) at a landscape scale. Several studies have been conducted using drone-based remote sensing to quantitatively assess the impacts of wind erosion on the vegetation communities and landforms in drylands. In this study, first, five difficulties in conducting wind erosion research through data collection from fieldwork are summarized: insufficient samples, spatial displacement with auxiliary datasets, missing volumetric information, a unidirectional view, and spatially inexplicit input. Then, five possible applications—to provide a reliable and valid sample set, to mitigate the spatial offset, to monitor soil elevation change, to evaluate the directional property of land cover, and to make spatially explicit input for ecological models—of drone-based remote sensing products are suggested. To sum up, drone-based remote sensing has become a useful method to research wind erosion in drylands, and can solve the issues caused by using data collected from fieldwork. For wind erosion research in drylands, we suggest that a drone-based remote sensing product should be used as a complement to field measurements.
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Anderson, R. C., P. C. Shanks, L. A. Kritis, and M. G. Trani. "Supporting Remote Sensing Research with Small Unmanned Aerial Systems." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 51–56. http://dx.doi.org/10.5194/isprsarchives-xl-1-51-2014.
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We describe several remote sensing research projects supported with small Unmanned Aerial Systems (sUAS) operated by the NGA Basic and Applied Research Office. These sUAS collections provide data supporting Small Business Innovative Research (SBIR), NGA University Research Initiative (NURI), and Cooperative Research And Development Agreements (CRADA) efforts in addition to inhouse research. Some preliminary results related to 3D electro-optical point clouds are presented, and some research goals discussed. Additional details related to the autonomous operational mode of both our multi-rotor and fixed wing small Unmanned Aerial System (sUAS) platforms are presented.
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Yu, H. S., B. V. Jackson, P. P. Hick, A. Buffington, D. Odstrcil, C. C. Wu, J. A. Davies, M. M. Bisi, and M. Tokumaru. "3D Reconstruction of Interplanetary Scintillation (IPS) Remote-Sensing Data: Global Solar Wind Boundaries for Driving 3D-MHD Models." Solar Physics 290, no. 9 (April 29, 2015): 2519–38. http://dx.doi.org/10.1007/s11207-015-0685-0.
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Robiati, Eyre, Vanneschi, Francioni, Venn, and Coggan. "Application of Remote Sensing Data for Evaluation of Rockfall Potential within a Quarry Slope." ISPRS International Journal of Geo-Information 8, no. 9 (August 22, 2019): 367. http://dx.doi.org/10.3390/ijgi8090367.
Full textAbstract:
In recent years data acquisition from remote sensing has become readily available to the quarry sector. This study demonstrates how such data may be used to evaluate and back analyse rockfall potential of a legacy slope in a blocky rock mass. Use of data obtained from several aerial LiDAR (Light Detection and Ranging) and photogrammetric campaigns taken over a number of years (2011 to date) provides evidence for potential rockfall evolution from a slope within an active quarry operation in Cornwall, UK. Further investigation, through analysis of point cloud data obtained from terrestrial laser scanning, was undertaken to characterise the orientation of discontinuities present within the rock slope. Aerial and terrestrial LiDAR data were subsequently used for kinematic analysis, production of surface topography models and rockfall trajectory analyses using both 2D and 3D numerical simulations. The results of an Unmanned Aerial Vehicle (UAV)-based 3D photogrammetric analysis enabled the reconstruction of high resolution topography, allowing one to not only determine geometrical properties of the slope surface and geo-mechanical characterisation but provide data for validation of numerical simulations. The analysis undertaken shows the effectiveness of the existing rockfall barrier, while demonstrating how photogrammetric data can be used to inform back analyses of the underlying failure mechanism and investigate potential runout.