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1
Liu, Q., X. Li, G. Liu, C. Huang, H. Li e X. Guan. "SHARPENDING OF THE VNIR AND SWIR BANDS OF THE WIDE BAND SPECTRAL IMAGER ONBOARD TIANGONG-II IMAGERY USING THE SELECTED BANDS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (30 aprile 2018): 1085–92. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1085-2018.
Abstract (sommario):
The Tiangong-II space lab was launched at the Jiuquan Satellite Launch Center of China on September 15, 2016. The Wide Band Spectral Imager (WBSI) onboard the Tiangong-II has 14 visible and near-infrared (VNIR) spectral bands covering the range from 403–990 nm and two shortwave infrared (SWIR) bands covering the range from 1230–1250 nm and 1628–1652 nm respectively. In this paper the selected bands are proposed which aims at considering the closest spectral similarities between the VNIR with 100 m spatial resolution and SWIR bands with 200 m spatial resolution. The evaluation of Gram-Schmidt transform (GS) sharpening techniques embedded in ENVI software is presented based on four types of the different low resolution pan band. The experimental results indicated that the VNIR band with higher CC value with the raw SWIR Band was selected, more texture information was injected the corresponding sharpened SWIR band image, and at that time another sharpened SWIR band image preserve the similar spectral and texture characteristics to the raw SWIR band image.
2
Purwanto, Anang Dwi, e Wikanti Asriningrum. "IDENTIFICATION OF MANGROVE FORESTS USING MULTISPECTRAL SATELLITE IMAGERIES". International Journal of Remote Sensing and Earth Sciences (IJReSES) 16, n. 1 (30 ottobre 2019): 63. http://dx.doi.org/10.30536/j.ijreses.2019.v16.a3097.
Abstract (sommario):
The visual identification of mangrove forests is greatly constrained by combinations of RGB composite. This research aims to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using the Optimum Index Factor (OIF) method. The OIF method uses the standard deviation value and correlation coefficient from a combination of three image bands. The image data comprise Landsat 8 imagery acquired on 30 May 2013, Sentinel 2A imagery acquired on 18 March 2018 and images from SPOT 6 acquired on 10 January 2015. The results show that the band composites of 564 (NIR+SWIR+Red) from Landsat 8 and 8a114 (Vegetation Red Edge+SWIR+Red) from Sentinel 2A are the best RGB composites for identifying mangrove forest, in addition to those of 341 (Red+NIR+Blue) from SPOT 6. The near-infrared (NIR) and short-wave infrared (SWIR) bands play an important role in determining mangrove forests. The properties of vegetation are reflected strongly at the NIR wavelength and the SWIR band is very sensitive to evaporation and the identification of wetlands.
3
Xu, Dandan, Dong Zhang, Dan Shi e Zhaoqing Luan. "Automatic Extraction of Open Water Using Imagery of Landsat Series". Water 12, n. 7 (6 luglio 2020): 1928. http://dx.doi.org/10.3390/w12071928.
Abstract (sommario):
Open surface freshwater is an important resource for terrestrial ecosystems. However, climate change, seasonal precipitation cycling, and anthropogenic activities add high variability to its availability. Thus, timely and accurate mapping of open surface water is necessary. In this study, a methodology based on the concept of spatial autocorrelation was developed for automatic water extraction from Landsat series images using Taihu Lake in south-eastern China as an example. The results show that this method has great potential to extract continuous open surface water automatically, even when the water surface is covered by floating vegetation or algal blooms. The results also indicate that the second shortwave-infrared band (SWIR2) band performs best for water extraction when water is turbid or covered by surficial vegetation. Near-infrared band (NIR), first shortwave-infrared band (SWIR1), and SWIR2 have consistent extraction success when the water surface is not covered by vegetation. Low filter image processing greatly overestimated extracted water bodies, and cloud and image salt and pepper issues have a large impact on water extraction using the methods developed in this study.
4
Rostami, Amirhossein, Reza Shah-Hosseini, Shabnam Asgari, Arastou Zarei, Mohammad Aghdami-Nia e Saeid Homayouni. "Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning". Remote Sensing 14, n. 4 (17 febbraio 2022): 992. http://dx.doi.org/10.3390/rs14040992.
Abstract (sommario):
Active fires are devastating natural disasters that cause socio-economical damage across the globe. The detection and mapping of these disasters require efficient tools, scientific methods, and reliable observations. Satellite images have been widely used for active fire detection (AFD) during the past years due to their nearly global coverage. However, accurate AFD and mapping in satellite imagery is still a challenging task in the remote sensing community, which mainly uses traditional methods. Deep learning (DL) methods have recently yielded outstanding results in remote sensing applications. Nevertheless, less attention has been given to them for AFD in satellite imagery. This study presented a deep convolutional neural network (CNN) “MultiScale-Net” for AFD in Landsat-8 datasets at the pixel level. The proposed network had two main characteristics: (1) several convolution kernels with multiple sizes, and (2) dilated convolution layers (DCLs) with various dilation rates. Moreover, this paper suggested an innovative Active Fire Index (AFI) for AFD. AFI was added to the network inputs consisting of the SWIR2, SWIR1, and Blue bands to improve the performance of the MultiScale-Net. In an ablation analysis, three different scenarios were designed for multi-size kernels, dilation rates, and input variables individually, resulting in 27 distinct models. The quantitative results indicated that the model with AFI-SWIR2-SWIR1-Blue as the input variables, using multiple kernels of sizes 3 × 3, 5 × 5, and 7 × 7 simultaneously, and a dilation rate of 2, achieved the highest F1-score and IoU of 91.62% and 84.54%, respectively. Stacking AFI with the three Landsat-8 bands led to fewer false negative (FN) pixels. Furthermore, our qualitative assessment revealed that these models could detect single fire pixels detached from the large fire zones by taking advantage of multi-size kernels. Overall, the MultiScale-Net met expectations in detecting fires of varying sizes and shapes over challenging test samples.
5
Ye, Bei, Shufang Tian, Qiuming Cheng e Yunzhao Ge. "Application of Lithological Mapping Based on Advanced Hyperspectral Imager (AHSI) Imagery Onboard Gaofen-5 (GF-5) Satellite". Remote Sensing 12, n. 23 (6 dicembre 2020): 3990. http://dx.doi.org/10.3390/rs12233990.
Abstract (sommario):
The Advanced Hyperspectral Imager (AHSI), carried by the Gaofen-5 (GF-5) satellite, is the first hyperspectral sensor that simultaneously offers broad coverage and a broad spectrum. Meanwhile, deep-learning-based approaches are emerging to manage the growing volume of data produced by satellites. However, the application potential of GF-5 AHSI imagery in lithological mapping using deep-learning-based methods is currently unknown. This paper assessed GF-5 AHSI imagery for lithological mapping in comparison with Shortwave Infrared Airborne Spectrographic Imager (SASI) data. A multi-scale 3D deep convolutional neural network (M3D-DCNN), a hybrid spectral CNN (HybridSN), and a spectral–spatial unified network (SSUN) were selected to verify the applicability and stability of deep-learning-based methods through comparison with support vector machine (SVM) based on six datasets constructed by GF-5 AHSI, Sentinel-2A, and SASI imagery. The results show that all methods produce classification results with accuracy greater than 90% on all datasets, and M3D-DCNN is both more accurate and more stable. It can produce especially encouraging results by just using the short-wave infrared wavelength subset (SWIR bands) of GF-5 AHSI data. Accordingly, GF-5 AHSI imagery could provide impressive results and its SWIR bands have a high signal-to-noise ratio (SNR), which meets the requirements of large-scale and large-area lithological mapping. And M3D-DCNN method is recommended for use in lithological mapping based on GF-5 AHSI hyperspectral data.
6
Casana, Jesse, e Carolin Ferwerda. "Drone-Acquired Short-Wave Infrared (SWIR) Imagery in Landscape Archaeology: An Experimental Approach". Remote Sensing 16, n. 10 (9 maggio 2024): 1671. http://dx.doi.org/10.3390/rs16101671.
Abstract (sommario):
Many rocks, minerals, and soil types reflect short-wave infrared (SWIR) imagery (900–2500 nm) in distinct ways, and geologists have long relied on this property to aid in the mapping of differing surface lithologies. Although surface archaeological features including artifacts, anthrosols, or structural remains also likely reflect SWIR wavelengths of light in unique ways, archaeological applications of SWIR imagery are rare, largely due to the low spatial resolution and high acquisition costs of these data. Fortunately, a new generation of compact, drone-deployable sensors now enables the collection of ultra-high-resolution (<10 cm), hyperspectral (>100 bands) SWIR imagery using a consumer-grade drone, while the analysis of these complex datasets is now facilitated by powerful imagery-processing software packages. This paper presents an experimental effort to develop a methodology that would allow archaeologists to collect SWIR imagery using a drone, locate surface artifacts in the resultant data, and identify different artifact types in the imagery based on their reflectance values across the 900–1700 nm spectrum. Our results illustrate both the potential of this novel approach to exploring the archaeological record, as we successfully locate and characterize many surface artifacts in our experimental study, while also highlighting challenges in successful data collection and analysis, largely related to current limitations in sensor and drone technology. These findings show that as underlying hardware sees continued improvements in the coming years, drone-acquired SWIR imagery can become a powerful tool for the discovery, documentation, and analysis of archaeological landscapes.
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Feng, Haixia, Chao Chen, Heng Dong, Jinliang Wang e Qingye Meng. "Modified Shortwave Infrared Perpendicular Water Stress Index: A Farmland Water Stress Monitoring Method". Journal of Applied Meteorology and Climatology 52, n. 9 (settembre 2013): 2024–32. http://dx.doi.org/10.1175/jamc-d-12-0164.1.
Abstract (sommario):
AbstractCrop water stress monitoring by remote sensing has been the focus of numerous studies. In this paper, specifically red (630–690 nm) and shortwave infrared (SWIR; 1550–1750 nm) wavelength bands are identified to monitor farmland water stress, and a method [modified shortwave infrared perpendicular water stress index (MSPSI)] is developed that is based on the spectral space constructed by SWIR − Red (Rd) and SWIR + Red (Rs). The MSPSI stayed at mostly the same water stress level for full vegetation coverage cases with high vegetation water content and saturated bare soil as well as full vegetation coverage with extremely low vegetation water and dry bare soil in the Rs–Rd spectral feature space. This approach makes the water stress conditions between different covers comparable and the MSPSI applicable to farmland water stress monitoring in different vegetation covers throughout the growing season. To validate the proposed index, the MSPSI calculated from Thematic Mapper images and Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m reflectance products (from March to October) in the Ningxia Hui Autonomous Region was compared with the ground-measured soil moisture content at different depths. It is evident from the results that the MSPSI derived from satellite imageries is highly correlated with ground-measured soil moisture at different depths (7.6 and 10 cm), with coefficients of determination R2 of 0.666, 0.512, 0.576, 0.361, 0.383, 0.391, 0.357, 0.410, and 0.418. The paper concludes that MSPSI is a promising index for crop water stress monitoring throughout the growing season.
8
WANG, Yue-Ming, Qian ZHU, Jian-Yu WANG e Xiao-Qiong ZHUANG. "Characterization of background radiation in SWIR hyperspectral imager". JOURNAL OF INFRARED AND MILLIMETER WAVES 30, n. 3 (20 marzo 2012): 279–83. http://dx.doi.org/10.3724/sp.j.1010.2011.00279.
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Reddy, S. L. K., C. V. Rao, P. R. Kumar, R. V. G. Anjaneyulu e B. G. Krishna. "A NOVEL METHOD FOR WATER AND WATER CANAL EXTRACTION FROM LANDSAT-8 OLI IMAGERY". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (19 novembre 2018): 323–28. http://dx.doi.org/10.5194/isprs-archives-xlii-5-323-2018.
Abstract (sommario):
<p><strong>Abstract.</strong> Constituents of hydrologic network, River and water canals play a key role in Agriculture for cultivation, Industrial activities and urban planning. Remote sensing images can be effectively used for water canal extraction, which significantly improves the accuracy and reduces the cost involved in mapping using conventional means. Using remote sensing data, the water Index (WI), Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) are used in extracting the water bodies. These techniques are aimed at water body detection and need to be complemented with additional information for the extraction of complete water canal networks. The proposed index MNDWI-2 is able to find the water bodies and water canals as well from the Landsat-8 OLI imagery and is based on the SWIR2 band. In this paper, we use Level-1 precision terrain corrected OLI imagery at 30 meter spatial resolution. The proposed MNDWI-2 index is derived using SWIR2 (B7) band and Green (B3) band. The usage of SWIR2 band over SWIR1 results in very low reflectance values for water features, detection of shallow water and delineation of water features with rest of the features in the image. The computed MNDWI-2 index values are threshold by making the values greater than zero as 1 and less than zero as zero. The binarised values of 1 represent the water bodies and 0 represent the non-water body. This normalized index detects the water bodies and canals as well as vegetation which appears in the form of noise. The vegetation from the MNDWI-2 image is removed by using the NDVI index, which is calculated using the Top of Atmosphere (TOA) corrected images. The paper presents the results of water canal extraction in comparison with the major available indexes. The proposed index can be used for water and water canal extraction from L8 OLI imagery, and can be extended for other high resolution sensors.</p>
10
Platnick, Steven, Kerry Meyer, Nandana Amarasinghe, Galina Wind, Paul A. Hubanks e Robert E. Holz. "Sensitivity of Multispectral Imager Liquid Water Cloud Microphysical Retrievals to the Index of Refraction". Remote Sensing 12, n. 24 (19 dicembre 2020): 4165. http://dx.doi.org/10.3390/rs12244165.
Abstract (sommario):
A cloud property retrieved from multispectral imagers having spectral channels in the shortwave infrared (SWIR) and/or midwave infrared (MWIR) is the cloud effective particle radius (CER), a radiatively relevant weighting of the cloud particle size distribution. The physical basis of the CER retrieval is the dependence of SWIR/MWIR cloud reflectance on the cloud particle single scattering albedo, which in turn depends on the complex index of refraction of bulk liquid water (or ice) in addition to the cloud particle size. There is a general consistency in the choice of the liquid water index of refraction by the cloud remote sensing community, largely due to the few available independent datasets and compilations. Here we examine the sensitivity of CER retrievals to the available laboratory index of refraction datasets in the SWIR and MWIR using the retrieval software package that produces NASA’s standard Moderate Resolution Imaging Spectroradiometer (MODIS)/Visible Infrared Imaging Radiometer suite (VIIRS) continuity cloud products. The sensitivity study incorporates two laboratory index of refraction datasets that include measurements at supercooled water temperatures, one in the SWIR and one in the MWIR. Neither has been broadly utilized in the cloud remote sensing community. It is shown that these two new datasets can significantly change CER retrievals (e.g., 1–2 µm) relative to common datasets used by the community. Further, index of refraction data for a 265 K water temperature gives more consistent retrievals between the two spectrally distinct 2.2 µm atmospheric window channels on MODIS and VIIRS. As a result, 265 K values from the SWIR and MWIR index of refraction datasets were adopted for use in the production version of the continuity cloud product. The results indicate the need to better understand temperature-dependent bulk water absorption and uncertainties in these spectral regions.
11
Cha, Sungeun, Joongbin Lim, Kyoungmin Kim, Jongsoo Yim e Woo-Kyun Lee. "Uncovering the Potential of Multi-Temporally Integrated Satellite Imagery for Accurate Tree Species Classification". Forests 14, n. 4 (5 aprile 2023): 746. http://dx.doi.org/10.3390/f14040746.
Abstract (sommario):
In this study, prior to the launch of compact advanced satellite 500 (CAS500-4), which is an agriculture and forestry satellite, nine major tree species were classified using multi-temporally integrated imageries based on a random forest model using RapidEye and Sentinel-2. Six scenarios were devised considering the composition of the input dataset, and a random forest model was used to evaluate the accuracy of the different input datasets for each scenario. The highest accuracy, with accuracy values of 84.5% (kappa value: 0.825), was achieved by using RapidEye and Sentinel-2 spectral wavelengths along with gray-level co-occurrence matrix (GLCM) statistics (Scenario IV). In the variable importance analysis, the short-wave infrared (SWIR) band of Sentinel-2 and the GLCM statistics of RapidEye were found to be sequentially higher. This study proposes an optimal input dataset for tree species classification using the variance error range of GLCM statistics to establish an optimal range for window size calculation methodology. We also demonstrate the effectiveness of multi-temporally integrated satellite imageries in improving the accuracy of the random forest model, achieving an approximate improvement of 20.5%. The findings of this study suggest that combining the advantages of different satellite platforms and statistical methods can lead to significant improvements in tree species classification accuracy, which can contribute to better forest resource assessments and management strategies in the face of climate change.
12
He Hongxing, 何红星, 赵劲松 Zhao Jingsong e 潘顺臣 Pan Shunchen. "Common-Aperture Optical System for MWIR/SWIR Polarization Imager". Acta Optica Sinica 29, n. 4 (2009): 932–36. http://dx.doi.org/10.3788/aos20092904.0932.
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Aliyan, S. A., A. S. Bratanegara, H. M. Ihsan, A. J. Astari e L. Somantri. "identification of lithological characteristics using multispectral landsat 8 oli imagery in the cipatujah area, west java, indonesia". IOP Conference Series: Earth and Environmental Science 1089, n. 1 (1 novembre 2022): 012021. http://dx.doi.org/10.1088/1755-1315/1089/1/012021.
Abstract (sommario):
Abstract The Cipatujah area is part of the Southern Mountains of West Java which has diverse and unevenly distributed lithology. The lithology that dominates the Cipatujah and surrounding areas originate from the volcanic activities such as lava, volcanic breccias, tuffs, and intrusions. While the sedimentary rocks that compose them are limestone and sandstone rocks. The lithology that dominates the southern region is carbonate sedimentary rocks, which are represented by sandstone units. In the northern part, the lithologies are dominated by deposition results volcanic activity consists of various materials originating from andesitic lava units that extend to the east of the research area, while the volcanic breccia deposited from north to the west of the research area. There is a tuff unit layer above the volcanic breccia to the south. In the eastern area deposited carbonate rock units that form the karst landscape. Lithology characterization and determination of rock units in the Cipatujah area were carried out using image processing techniques from color composite bands from Landsat-8 (OLI) data. Geological analysis using SWIR-2 (7), SWIR-1 (6), and blue (2) composite bands and lithology using near-infrared (5) composites SWIR-1 (6), and SWIR-2 (7) bands. Then the analysis results are examined with geological data from the mapping that has been done before. Approach to band composite analysis by verifying geological data taken directly to help improve the identification and validation of better and more measured lithological distribution.
14
Cao, Zhicheng, Natalia A. Schmid, Shufen Cao e Liaojun Pang. "GMLM-CNN: A Hybrid Solution to SWIR-VIS Face Verification with Limited Imagery". Sensors 22, n. 23 (5 dicembre 2022): 9500. http://dx.doi.org/10.3390/s22239500.
Abstract (sommario):
Cross-spectral face verification between short-wave infrared (SWIR) and visible light (VIS) face images poses a challenge, which is motivated by various real-world applications such as surveillance at night time or in harsh environments. This paper proposes a hybrid solution that takes advantage of both traditional feature engineering and modern deep learning techniques to overcome the issue of limited imagery as encountered in the SWIR band. Firstly, the paper revisits the theory of measurement levels. Then, two new operators are introduced which act at the nominal and interval levels of measurement and are named the Nominal Measurement Descriptor (NMD) and the Interval Measurement Descriptor (IMD), respectively. A composite operator Gabor Multiple-Level Measurement (GMLM) is further proposed which fuses multiple levels of measurement. Finally, the fused features of GMLM are passed through a succinct and efficient neural network based on PCA. The network selects informative features and also performs the recognition task. The overall framework is named GMLM-CNN. It is compared to both traditional hand-crafted operators as well as recent deep learning-based models that are state-of-the-art, in terms of cross-spectral verification performance. Experiments are conducted on a dataset which comprises frontal VIS and SWIR faces acquired at varying standoffs. Experimental results demonstrate that, in the presence of limited data, the proposed hybrid method GMLM-CNN outperforms all the other methods.
15
Li, Jun, Zhaocong Wu, Zhongwen Hu, Zilong Li, Yisong Wang e Matthieu Molinier. "Deep Learning Based Thin Cloud Removal Fusing Vegetation Red Edge and Short Wave Infrared Spectral Information for Sentinel-2A Imagery". Remote Sensing 13, n. 1 (5 gennaio 2021): 157. http://dx.doi.org/10.3390/rs13010157.
Abstract (sommario):
Thin clouds seriously affect the availability of optical remote sensing images, especially in visible bands. Short-wave infrared (SWIR) bands are less influenced by thin clouds, but usually have lower spatial resolution than visible (Vis) bands in high spatial resolution remote sensing images (e.g., in Sentinel-2A/B, CBERS04, ZY-1 02D and HJ-1B satellites). Most cloud removal methods do not take advantage of the spectral information available in SWIR bands, which are less affected by clouds, to restore the background information tainted by thin clouds in Vis bands. In this paper, we propose CR-MSS, a novel deep learning-based thin cloud removal method that takes the SWIR and vegetation red edge (VRE) bands as inputs in addition to visible/near infrared (Vis/NIR) bands, in order to improve cloud removal in Sentinel-2 visible bands. Contrary to some traditional and deep learning-based cloud removal methods, which use manually designed rescaling algorithm to handle bands at different resolutions, CR-MSS uses convolutional layers to automatically process bands at different resolution. CR-MSS has two input/output branches that are designed to process Vis/NIR and VRE/SWIR, respectively. Firstly, Vis/NIR cloudy bands are down-sampled by a convolutional layer to low spatial resolution features, which are then concatenated with the corresponding features extracted from VRE/SWIR bands. Secondly, the concatenated features are put into a fusion tunnel to down-sample and fuse the spectral information from Vis/NIR and VRE/SWIR bands. Third, a decomposition tunnel is designed to up-sample and decompose the fused features. Finally, a transpose convolutional layer is used to up-sample the feature maps to the resolution of input Vis/NIR bands. CR-MSS was trained on 28 real Sentinel-2A image pairs over the globe, and tested separately on eight real cloud image pairs and eight simulated cloud image pairs. The average SSIM values (Structural Similarity Index Measurement) for CR-MSS results on Vis/NIR bands over all testing images were 0.69, 0.71, 0.77, and 0.81, respectively, which was on average 1.74% higher than the best baseline method. The visual results on real Sentinel-2 images demonstrate that CR-MSS can produce more realistic cloud and cloud shadow removal results than baseline methods.
16
Patel, P., H. Bhatt e A. K. Shukla. "Absolute Vicarious Calibration of recently launched Indian Meteorological Satellite: INSAT-3D imager". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (28 novembre 2014): 291–98. http://dx.doi.org/10.5194/isprsarchives-xl-8-291-2014.
Abstract (sommario):
Looking towards the advancements and popularity of remote sensing and an ever increasing need for the development of a variety of new and complex satellite sensors, it has become even more essential to continually upgrade the ability to provide absolute calibration of sensors. This article describes a simple procedure to implement post-launch calibration for VIS and SWIR channels of INSAT-3D imager over land site (Little Rann of Kutch (ROK), Gujarat) on three different days to account for characterization errors or undetermined post-launch changes in spectral response of the sensor. The measurements of field reflectance of study site (of extent ~6 km x 6 km) in the wavelength range 325–2500 nm, along with atmospheric parameters (Aerosol Optical Depth, Total Columnar Ozone, Water Vapor) and sensor spectral response functions, were input to the 6S radiative transfer model to simulate radiance at top of the atmosphere (TOA) for VIS and SWIR bands. The uncertainty in vicarious calibration coefficients due to measured spatial variability of field reflectance along with due to aerosol types were also computed for the INSAT-3D imager. The effect of surface anisotropy on TOA radiance was studied using a MODIS Bidirectional Reflectance Distribution Function (BRDF) product covering the experimental site. The results show that there is no indication of change in calibration coefficients in INSAT- 3D imager, for VIS and SWIR band over Little ROK. Comparison made between the INSAT-3D imager measured radiance and 6S simulated radiance. Analysis shows that for clear sky days, the INSAT-3D imager overestimates TOA radiance in the VIS band by 5.1 % and in the SWIR band by 11.7 % with respect to 6S simulated radiance. For these bands, in the inverse mode, the 6S corrected surface reflectance was closer to field surface reflectance. It was found that site spatial variability was a critical factor in estimating change in sensor calibration coefficients and influencing uncertainty in TOA radiance for Little ROK.
17
Rotovnik, Tomaz, Dejan Gacnik e Iztok Kramberger. "Miniaturized Multispectral VNIR/SWIR Imager for Small Satellites - System Design". IFAC-PapersOnLine 48, n. 10 (2015): 12–15. http://dx.doi.org/10.1016/j.ifacol.2015.08.100.
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Tuominen, S., R. Näsi, E. Honkavaara, A. Balazs, T. Hakala, N. Viljanen, I. Pölönen, H. Saari e J. Reinikainen. "TREE SPECIES RECOGNITION IN SPECIES RICH AREA USING UAV-BORNE HYPERSPECTRAL IMAGERY AND STEREO-PHOTOGRAMMETRIC POINT CLOUD". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W3 (20 ottobre 2017): 185–94. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w3-185-2017.
Abstract (sommario):
Recognition of tree species and geospatial information of tree species composition is essential for forest management. In this study we test tree species recognition using hyperspectral imagery from VNIR and SWIR camera sensors in combination with 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum forest with a high number of tree species was used as a test area. The imagery was acquired from the test area using UAV-borne cameras. Hyperspectral imagery was calibrated for providing a radiometrically corrected reflectance mosaic, which was tested along with the original uncalibrated imagery. Alternative estimators were tested for predicting tree species and genus, as well as for selecting an optimal set of remote sensing features for this task. All tested estimators gave similar trend in the results: the calibrated reflectance values performed better in predicting tree species and genus compared to uncorrected hyperspectral pixel values. Furthermore, the combination of VNIR, SWIR and 3D features performed better than any of the data sets individually, with calibrated reflectances and original pixel values alike. The highest proportion of correctly classified trees was achieved using calibrated reflectance features from VNIR and SWIR imagery together with 3D point cloud features: 0.823 for tree species and 0.869 for tree genus.
19
Zhang, Tianyuan, Huazhong Ren, Qiming Qin e Yuanheng Sun. "Snow Cover Monitoring with Chinese Gaofen-4 PMS Imagery and the Restored Snow Index (RSI) Method: Case Studies". Remote Sensing 10, n. 12 (23 novembre 2018): 1871. http://dx.doi.org/10.3390/rs10121871.
Abstract (sommario):
Snow cover is an essential climate variable of the Global Climate Observing System. Gaofen-4 (GF-4) is the first Chinese geostationary satellite to obtain optical imagery with high spatial and temporal resolution, which presents unique advantages in snow cover monitoring. However, the panchromatic and multispectral sensor (PMS) onboard GF-4 lacks the shortwave infrared (SWIR) band, which is crucial for snow cover detection. To reach the potential of GF-4 PMS in snow cover monitoring, this study developed a novel method termed the restored snow index (RSI). The SWIR reflectance of snow cover is restored firstly, and then the RSI is calculated with the restored reflectance. The distribution of snow cover can be mapped with a threshold, which should be adjusted according to actual situations. The RSI was validated using two pairs of GF-4 PMS and Landsat-8 Operational Land Imager images. The validation results show that the RSI can effectively map the distribution of snow cover in these cases, and all of the classification accuracies are above 95%. Signal saturation slightly affects PMS images, but cloud contamination is an important limiting factor. Therefore, we propose that the RSI is an efficient method for monitoring snow cover from GF-4 PMS imagery without requiring the SWIR reflectance.
20
Li, Huifang, Liangpei Zhang, Huanfeng Shen e Pingxiang Li. "A Variational Gradient-based Fusion Method for Visible and SWIR Imagery". Photogrammetric Engineering & Remote Sensing 78, n. 9 (1 settembre 2012): 947–58. http://dx.doi.org/10.14358/pers.78.9.947.
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Xu, Yue, Shao-Biao Xie, Jian Liang, De-Xin Sun, Nai-Ming Qi e Yin-Nian Liu. "A Novel Method to Remove Interference Fringes for Hyperspectral SWIR Imagers". IEEE Transactions on Geoscience and Remote Sensing 58, n. 11 (novembre 2020): 7580–88. http://dx.doi.org/10.1109/tgrs.2020.2981640.
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Oliveira, R. A., R. Näsi, P. Korhonen, A. Mustonen, O. Niemeläinen, N. Koivumäki, T. Hakala, J. Suomalainen, J. Kaivosoja e E. Honkavaara. "HYPERSPECTRAL UAS IMAGERY FOR GRASS SWARDS BIOMASS AND NITROGEN ESTIMATION". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (14 dicembre 2023): 1861–66. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1861-2023.
Abstract (sommario):
Abstract. Monitoring agricultural grass fields is particularly important for meat and milk production in Northern Europe, where three harvests occur during a growing season to maximize yields. Reliable data on forage, including biomass and nitrogen concentration, are essential for making informed decisions regarding seed mixtures, fertilizer rates, and harvest timing. Miniaturized hyperspectral cameras mounted on unmanned aerial systems (UAS) have become increasingly accessible and efficient. These cameras, operating in the visible to near-infrared (VNIR) range, have shown potential in estimating grass sward quantity and feeding quality. Additional advancements in hyperspectral technology have emerged the short-wave infrared (SWIR) range for UAS applications, previously utilized mainly in laboratory and aircraft-based systems. This study aims to explore the potential of VNIR and SWIR hyperspectral UAS-based remote sensing in biomass and nitrogen estimation during primary and re-growth stages. Grass fresh yield and nitrogen concentration prediction models were built after selecting the most significant features from the cameras to cope with the high dimensionality of the data. Using best features and machine learning, both fresh yield and nitrogen concentration were estimated with normalized root mean square error better than 10%. This work contributes to the development of accurate remote sensing techniques, supporting sustainable agricultural practices and climate change studies.
23
Gossn, Juan, Kevin Ruddick e Ana Dogliotti. "Atmospheric Correction of OLCI Imagery over Extremely Turbid Waters Based on the Red, NIR and 1016 nm Bands and a New Baseline Residual Technique". Remote Sensing 11, n. 3 (22 gennaio 2019): 220. http://dx.doi.org/10.3390/rs11030220.
Abstract (sommario):
A common approach to the pixel-by-pixel atmospheric correction of satellite water colour imagery is to calculate aerosol and water reflectance at two spectral bands, typically in the near infra-red (NIR, 700–1000 nm) or the short-wave-infra-red (SWIR, 1000–3000 nm), and then extrapolate aerosol reflectance to shorter wavelengths. For clear waters, this can be achieved simply for NIR bands, where the water reflectance can be assumed negligible i.e., the “black water” assumption. For moderately turbid waters, either the NIR water reflectance, which is non-negligible, must be modelled or longer wavelength SWIR bands, with negligible water reflectance, must be used. For extremely turbid waters, modelling of non-zero NIR water reflectance becomes uncertain because the spectral slopes of water and aerosol reflectance in the NIR become similar, making it difficult to distinguish between them. In such waters the use of SWIR bands is definitely preferred and the use of the MODIS bands at 1240 nm and 2130 nm is clearly established although, on many sensors such as the Ocean and Land Colour Instrument (OLCI), such SWIR bands are not included. Instead, a new, cheaper SWIR band at 1016 nm is available on OLCI with potential for much better atmospheric correction over extremely turbid waters. That potential is tested here. In this work, we demonstrate that for spectrally-close band triplets (such as OLCI bands at 779–865–1016 nm), the Rayleigh-corrected reflectance of the triplet’s “middle” band after baseline subtraction (or baseline residual, BLR) is essentially independent of the atmospheric conditions. We use the three BLRs defined by three consecutive band triplets of the group of bands 620–709–779–865–1016 nm to calculate water reflectance and hence aerosol reflectance at these wavelengths. Comparison with standard atmospheric correction algorithms shows similar performance in moderately turbid and clear waters and a considerable improvement in extremely turbid waters.
24
Zhang, Meng, Xuhong Wang, Chenlie Shi e Dajiang Yan. "Automated Glacier Extraction Index by Optimization of Red/SWIR and NIR /SWIR Ratio Index for Glacier Mapping Using Landsat Imagery". Water 11, n. 6 (12 giugno 2019): 1223. http://dx.doi.org/10.3390/w11061223.
Abstract (sommario):
Glaciers are recognized as key indicators of climate change on account of their sensitive reaction to minute climate variations. Extracting more accurate glacier boundaries from satellite data has become increasingly popular over the past decade, particularly when glacier outlines are regarded as a basis for change assessment. Automated multispectral glacier mapping methods based on Landsat imagery are more accurate, efficient and repeatable compared with previous glacier classification methods. However, some challenges still exist in regard to shadowed areas, clouds, water, and debris cover. In this study, a new index called the automated glacier extraction index (AGEI) is proposed to reduce water and shadow classification errors and improve the mapping accuracy of debris-free glaciers using Landsat imagery. Four test areas in China were selected and the performances of four commonly used methods: Maximum-likelihood supervised classification (ML), normalized difference snow and ice index (NDSI), single-band ratios Red/SWIR, and NIR/SWIR, were compared with the AGEI. Multiple thresholds identified by inspecting the shadowed glacier areas were tested to determine an optimal threshold. The confusion matrix, sub-pixel analysis, and plot-scale validation were calculated to evaluate the accuracies of glacier maps. The overall accuracies (OAs) created by AGEI were the highest compared to the four existing automatic methods. The sub-pixel analysis revealed that AGEI was the most accurate method for classifying glacier edge mixed pixels. Plot-scale validation indicated AGEI was good at separating challenging features from glaciers and matched the actual distribution of debris-free glaciers most closely. Therefore, the AGEI with an optimal threshold can be used for mapping debris-free glaciers with high accuracy, particularly in areas with shadows and water features.
25
Park, Honglyun, e Jaewan Choi. "Mineral Detection Using Sharpened VNIR and SWIR Bands of Worldview-3 Satellite Imagery". Sustainability 13, n. 10 (14 maggio 2021): 5518. http://dx.doi.org/10.3390/su13105518.
Abstract (sommario):
Worldview-3 satellite imagery provides panchromatic images with a high spatial resolution and visible near infrared (VNIR) and shortwave infrared (SWIR) bands with a low spatial resolution. These images can be used for various applications such as environmental analysis, urban monitoring and surveying for sustainability. In this study, mineral detection was performed using Worldview-3 satellite imagery. A pansharpening technique was applied to the spatial resolution of the panchromatic image to effectively utilize the VNIR and SWIR bands of Worldview-3 satellite imagery. The following representative similarity analysis techniques were implemented for the mineral detection: the spectral angle mapper (SAM), spectral information divergence (SID) and the normalized spectral similarity score (NS3). In addition, pixels that could be estimated to indicate minerals were calculated by applying an empirical threshold to each similarity analysis result. A majority voting technique was applied to the results of each similarity analysis and pixels estimated to indicate minerals were finally selected. The results of each similarity analysis were compared to evaluate the accuracy of the proposed methods. From that comparison, it could be confirmed that false negative and false positive rates decreased when the methods proposed in the present study were applied.
26
Fernández, Roemi, Héctor Montes e Carlota Salinas. "VIS-NIR, SWIR and LWIR Imagery for Estimation of Ground Bearing Capacity". Sensors 15, n. 6 (15 giugno 2015): 13994–4015. http://dx.doi.org/10.3390/s150613994.
27
Meola, Joseph, Michael T. Eismann, Randolph L. Moses e Joshua N. Ash. "Application of Model-Based Change Detection to Airborne VNIR/SWIR Hyperspectral Imagery". IEEE Transactions on Geoscience and Remote Sensing 50, n. 10 (ottobre 2012): 3693–706. http://dx.doi.org/10.1109/tgrs.2012.2186305.
28
Sanders, Lee C., John R. Schott e Rolando Raqueño. "A VNIR/SWIR atmospheric correction algorithm for hyperspectral imagery with adjacency effect". Remote Sensing of Environment 78, n. 3 (dicembre 2001): 252–63. http://dx.doi.org/10.1016/s0034-4257(01)00219-x.
29
Abioye, A. E., E. Laroche-Pinel, B. Sams, B. Corales, K. Vasquez, V. Cianciola e L. Brillante. "Grape composition assessment using NIR/SWIR hyperspectral imagery acquired from a UTV". Acta Horticulturae, n. 1395 (maggio 2024): 351–58. http://dx.doi.org/10.17660/actahortic.2024.1395.46.
30
Francis Emeka Egobueze, Eteh Desmond Rowland e Debekeme Silver Ebizimo. "Multispectral imagery for detection and monitoring of vegetation affected by oil spills and migration pattern in Niger Delta Region, Nigeria". World Journal of Advanced Research and Reviews 15, n. 1 (30 luglio 2022): 447–58. http://dx.doi.org/10.30574/wjarr.2022.15.1.0682.
Abstract (sommario):
Oil spills in the Niger Delta area can be detected and monitored using this novel technique. Landsat 5 and 8 images were used to assess various vegetation stress, such as Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, Atmospheric Resistant Vegetation Index, Green Near Infrared and Green Short-wave Infrared, from the spill site in 2019, non-spill site (1992 pre-oil spill) and 2020 post-oil spill events, respectively. There is a substantial difference (p-value <0.005) in the vegetation conditions at the Spill Site and the non-spill Site in 2020 in terms of NDWI, SAVI, ARVI2 and G-NIR and G-SWIR. NDVI, SAVI, ARVI2, G-NIR, G-SWIR, and G-SWIR. There is a very significant difference in vegetation conditions between the pre-spill event and the post-spill event in 2020 (p-value <0.005). The oil spills' migration patterns and flow directions indicate from north to south along the runoff water using SRTM data. The sentinel 1 data revealed visualization of the flooded areas, including water surfaces that are stable around oil pipelines and the surroundings, using calibration threshold and the RGB band method to distinguish flooded areas from permanent water bodies. This was used to map areas affected by the oil spill on land and water bodies for proper environmental assessment before and during the flood of the oil spill environment. Multi spectral imagery is therefore a veritable tool for detection, response and monitoring of oil spills from pipelines.
31
Wang, Lei, Yang Chen, Luliang Tang, Rongshuang Fan e Yunlong Yao. "Object-Based Convolutional Neural Networks for Cloud and Snow Detection in High-Resolution Multispectral Imagers". Water 10, n. 11 (15 novembre 2018): 1666. http://dx.doi.org/10.3390/w10111666.
Abstract (sommario):
Cloud and snow detection is one of the most significant tasks for remote sensing image processing. However, it is a challenging task to distinguish between clouds and snow in high-resolution multispectral images due to their similar spectral distributions. The shortwave infrared band (SWIR, e.g., Sentinel-2A 1.55–1.75 µm band) is widely applied to the detection of snow and clouds. However, high-resolution multispectral images have a lack of SWIR, and such traditional methods are no longer practical. To solve this problem, a novel convolutional neural network (CNN) to classify cloud and snow on an object level is proposed in this paper. Specifically, a novel CNN structure capable of learning cloud and snow multiscale semantic features from high-resolution multispectral imagery is presented. In order to solve the shortcoming of “salt-and-pepper” in pixel level predictions, we extend a simple linear iterative clustering algorithm for segmenting high-resolution multispectral images and generating superpixels. Results demonstrated that the new proposed method can with better precision separate the cloud and snow in the high-resolution image, and results are more accurate and robust compared to the other methods.
32
Trishchenko, Alexander P. "Solar Irradiance and Effective Brightness Temperature for SWIR Channels of AVHRR/NOAA and GOES Imagers". Journal of Atmospheric and Oceanic Technology 23, n. 2 (1 febbraio 2006): 198–210. http://dx.doi.org/10.1175/jtech1850.1.
Abstract (sommario):
Abstract Satellite observations in the shortwave infrared (SWIR) part of spectrum between 3.5 and 4.0 μm deliver critically important information for many applications. The satellite signal in this spectral band consists of solar-reflected radiation and thermal radiation emitted by surface, clouds, and atmosphere. Accurate retrievals require precise knowledge of solar irradiance values within a channel's bandwidth. The magnitudes of solar irradiance for shortwave infrared channels (3.7–3.9 μm) for the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration-7 (NOAA-7) to NOAA-18 satellites and the Geostationary Operational Environmental Satellite-8 (GOES-8) to GOES-12 are considered in this paper. Four recent solar reference spectra [those of Kurucz, Gueymard, the American Society for Testing and Materials (ASTM), and Wehrli] are analyzed to determine uncertainties in the knowledge of solar irradiance values for SWIR channels of the listed sensors. Because thermal radiation is frequently converted to effective blackbody temperature for analysis, computations, and calibration purposes, it is proposed here to express band-limited solar irradiance values in terms of brightness temperature as well. It is shown that band-limited solar irradiance for AVHRR radiometers expressed in terms of blackbody equivalent brightness temperature correspond to the range 355–360 K, and vary around 345 K for the SWIR channels of the GOES imagers. The values of band-limited solar irradiance and brightness temperatures are provided for various reference solar spectra. The relative differences in band-limited solar irradiance computed for the considered reference solar spectra are between 0% and 2.5%. Differences expressed in terms of brightness temperatures may reach 0.8 K. The results for the ASTM and the Kurucz reference spectra agree within 0.1% relative difference. Parameters of linear fits relating effective brightness temperatures and spectral radiance equivalent temperatures are also determined for all sensors. They are required for precise radiance–temperature and temperature–radiance conversion through Planck's functions in the case of the finite spectral response of real sensors.
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Kim, Mijin, Robert C. Levy, Lorraine A. Remer, Shana Mattoo e Pawan Gupta. "Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager". Atmospheric Measurement Techniques 17, n. 7 (4 aprile 2024): 1913–39. http://dx.doi.org/10.5194/amt-17-1913-2024.
Abstract (sommario):
Abstract. Originally developed for the moderate resolution imaging spectroradiometer (MODIS) in polar, sun-synchronous low earth orbit (LEO), the Dark Target (DT) aerosol retrieval algorithm relies on the assumption of a surface reflectance parameterization (SRP) over land surfaces. Specifically for vegetated and dark-soiled surfaces, values of surface reflectance in blue and red visible-wavelength bands are assumed to be nearly linearly related to each other and to the value in a shortwave infrared (SWIR) wavelength band. This SRP also includes dependencies on scattering angle and a normalized difference vegetation index computed from two SWIR bands (NDVISWIR). As the DT retrieval algorithm is being ported to new sensors to continue and expand the aerosol data record, we assess whether the MODIS-assumed SRP can be used for these sensors. Here, we specifically assess SRP for the Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite (GOES)-16/East (ABIE). First, we find that using MODIS-based SRP leads to higher biases and artificial diurnal signatures in aerosol optical depth (AOD) retrievals from ABIE. The primary reason appears to be that the geostationary orbit (GEO) encounters an entirely different set of observation geometry than does LEO, primarily with regard to solar angles coupled with fixed-view angles. Therefore, we have developed a new SRP for GEO that draws the angular shape of the surface bidirectional reflectance. We also introduce modifications to the parameterization of both red–SWIR and blue–red spectral relationships to include additional information. The revised red–SWIR SRP includes the solar zenith angle, NDVISWIR, and land-type percentage from an ancillary database. The blue–red SRP adds dependencies on the scattering angle and NDVISWIR. The new SRPs improve the AOD retrieval of ABIE in terms of overall less bias and mitigation of the overestimation around local noon. The average bias of the DT AOD compared to the Aerosol Robotic Network (AERONET) AOD shows a reduction from 0.08 to 0.03, while the bias of local solar noon decreases from 0.12 to 0.03. The agreement between the DT and AERONET AOD is established through a regression slope of 1.06 and a y intercept of 0.01 with a correlation coefficient of 0.74. By using the new SRP, the percentage of data falling within the expected error range (±0.05 % + 15 %) is notably increased from 54 % to 78 %.
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Astiti, Sagung Putri Chandra, Takahiro Osawa e I. Wayan Nuarsa. "IDENTIFICATION OF SHORELINE CHANGES USING SENTINEL 2 IMAGERY DATA IN CANGGU COASTAL AREA". ECOTROPHIC : Jurnal Ilmu Lingkungan (Journal of Environmental Science) 13, n. 2 (30 novembre 2019): 191. http://dx.doi.org/10.24843/ejes.2019.v13.i02.p07.
Abstract (sommario):
Coastal areas in the Canggu and Seminyak areas located in Badung Regency, Bali Province are very attractive tourism. The development of tourism has an impact on coastal conditions. The coastal conditions analyzed are changes in coastline that occurred during 2015-2019 using remote sensing. The satellite image data used in the analysis is Sentinel 2A image data that can be accessed for free with a spatial resolution of 10 meters. Image data processing is divided into three stages, namely preprocessing, processing, and post processing using Sentinel Application Platform (SNAP) software. The preprocessing stage includes the resampling, masking, and subset areas. The processing stage includes digitizing the coastal area, digitizing accuracy analysis using the Support Vector Machine (SVM) method, and the post processing stage including correction of shoreline changes. Bands in image data used for detection of coastal areas are band 8 (NIR), 8A (narrow NIR), 11 (SWIR), and 12 (SWIR). Based on the results of the analysis of shoreline changes carried out during 2015-2019, it was found that the average shoreline changes were 1.42 m / year with erosion conditions in which the dominant wind direction originated from the southwest towards the northeast coast of the sea of ??Bali. The results of digitizing the coastal area using the Fine Gaussian SVM method with the greatest accuracy value is 87.8%.
Keywords: Shoreline Change, Remote Sensing, Sentinel 2A, SVM, Wind Direction
35
Gerhards, Max, Martin Schlerf, Uwe Rascher, Thomas Udelhoven, Radoslaw Juszczak, Giorgio Alberti, Franco Miglietta e Yoshio Inoue. "Analysis of Airborne Optical and Thermal Imagery for Detection of Water Stress Symptoms". Remote Sensing 10, n. 7 (19 luglio 2018): 1139. http://dx.doi.org/10.3390/rs10071139.
Abstract (sommario):
High-resolution airborne thermal infrared (TIR) together with sun-induced fluorescence (SIF) and hyperspectral optical images (visible, near- and shortwave infrared; VNIR/SWIR) were jointly acquired over an experimental site. The objective of this study was to evaluate the potential of these state-of-the-art remote sensing techniques for detecting symptoms similar to those occurring during water stress (hereinafter referred to as ‘water stress symptoms’) at airborne level. Flights with two camera systems (Telops Hyper-Cam LW, Specim HyPlant) took place during 11th and 12th June 2014 in Latisana, Italy over a commercial grass (Festuca arundinacea and Poa pratense) farm with plots that were treated with an anti-transpirant agent (Vapor Gard®; VG) and a highly reflective powder (kaolin; KA). Both agents affect energy balance of the vegetation by reducing transpiration and thus reducing latent heat dissipation (VG) and by increasing albedo, i.e., decreasing energy absorption (KA). Concurrent in situ meteorological data from an on-site weather station, surface temperature and chamber flux measurements were obtained. Image data were processed to orthorectified maps of TIR indices (surface temperature (Ts), Crop Water Stress Index (CWSI)), SIF indices (F687, F780) and VNIR/SWIR indices (photochemical reflectance index (PRI), normalised difference vegetation index (NDVI), moisture stress index (MSI), etc.). A linear mixed effects model that respects the nested structure of the experimental setup was employed to analyse treatment effects on the remote sensing parameters. Airborne Ts were in good agreement (∆T < 0.35 K) compared to in situ Ts measurements. Maps and boxplots of TIR-based indices show diurnal changes: Ts was lowest in the early morning, increased by 6 K up to late morning as a consequence of increasing net radiation and air temperature (Tair) and remained stable towards noon due to the compensatory cooling effect of increased plant transpiration; this was also confirmed by the chamber measurements. In the early morning, VG treated plots revealed significantly higher Ts compared to control (CR) plots (p = 0.01), while SIF indices showed no significant difference (p = 1.00) at any of the overpasses. A comparative assessment of the spectral domains regarding their capabilities for water stress detection was limited due to: (i) synchronously overpasses of the two airborne sensors were not feasible, and (ii) instead of a real water stress occurrence only water stress symptoms were simulated by the chemical agents. Nevertheless, the results of the study show that the polymer di-1-p-menthene had an anti-transpiring effect on the plant while photosynthetic efficiency of light reactions remained unaffected. VNIR/SWIR indices as well as SIF indices were highly sensitive to KA, because of an overall increase in spectral reflectance and thus a reduced absorbed energy. On the contrary, the TIR domain was highly sensitive to subtle changes in the temperature regime as induced by VG and KA, whereas VNIR/SWIR and SIF domain were less affected by VG treatment. The benefit of a multi-sensor approach is not only to provide useful information about actual plant status but also on the causes of biophysical, physiological and photochemical changes.
36
Hannawald, Patrick, Carsten Schmidt, Sabine Wüst e Michael Bittner. "A fast SWIR imager for observations of transient features in OH airglow". Atmospheric Measurement Techniques 9, n. 4 (4 aprile 2016): 1461–72. http://dx.doi.org/10.5194/amt-9-1461-2016.
Abstract (sommario):
Abstract. Since December 2013 the new imaging system FAIM (Fast Airglow IMager) for the study of smaller-scale features (both in space and time) is in routine operation at the NDMC (Network for the Detection of Mesospheric Change) station at DLR (German Aerospace Center) in Oberpfaffenhofen (48.1° N, 11.3° E).Covering the brightest OH vibrational bands between 1 and 1.7 µm, this imaging system can acquire two frames per second. The field of view is approximately 55 km times 60 km at the mesopause heights. A mean spatial resolution of 200 m at a zenith angle of 45° and up to 120 m for zenith conditions are achieved. The observations show a large variety of atmospheric waves.This paper introduces the instrument and compares the FAIM data with spectrally resolved GRIPS (GRound-based Infrared P-branch Spectrometer) data. In addition, a case study of a breaking gravity wave event, which we assume to be associated with Kelvin–Helmholtz instabilities, is discussed.
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Gadal, S., e W. Ouerghemmi. "MORPHO-SPECTRAL RECOGNITION OF DENSE URBAN OBJECTS BY HYPERSPECTRAL IMAGERY". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (19 agosto 2015): 433–38. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-433-2015.
Abstract (sommario):
This paper presents a methodology for recognizing, identifying and classifying built objects in dense urban areas, using a morphospectral approach applied to VNIR/SWIR hyperspectral image (HySpex). This methodology contains several image processing steps: Principal Components Analysis and Laplacian enhancement, Feature Extraction of segmented build-up objects, and supervised classification from a morpho-spectral database (i.e. spectral and morphometric attributes). The Feature Extraction toolbox automatically generates a vector map of segmented buildings and an urban object-oriented morphometric database which is merged with an independent spectral database of urban objects. Each build-up object is spectrally identified and morphologically characterized thanks to the built-in morpho-spectral database.
38
Xu, Mingyue, Wenhui Zhou, Xingfa Shen, Yuhan Wang, Liangyan Mo e Junping Qiu. "Swin-TCNet: Swin-based temporal-channel cascade network for motor imagery iEEG signal recognition". Biomedical Signal Processing and Control 85 (agosto 2023): 104885. http://dx.doi.org/10.1016/j.bspc.2023.104885.
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WANG, Feng, Yi ZHOU, Fu-Li YAN, Shuo YANG e Cong DU. "ATMOSPHERIC CORRECTION ALGORITHM FOR MODIS IMAGERY OVER CASE Ⅱ WATERS BASED ON SWIR". JOURNAL OF INFRARED AND MILLIMETER WAVES 28, n. 5 (25 novembre 2009): 346–49. http://dx.doi.org/10.3724/sp.j.1010.2009.00346.
40
Hively, W. Dean, Jacob Shermeyer, Brian T. Lamb, Craig T. Daughtry, Miguel Quemada e Jason Keppler. "Mapping Crop Residue by Combining Landsat and WorldView-3 Satellite Imagery". Remote Sensing 11, n. 16 (9 agosto 2019): 1857. http://dx.doi.org/10.3390/rs11161857.
Abstract (sommario):
A unique, multi-tiered approach was applied to map crop-residue cover on the Eastern Shore of the Chesapeake Bay, United States. Field measurements of crop-residue cover were used to calibrate residue mapping using shortwave infrared (SWIR) indices derived from WorldView-3 imagery for a 12-km × 12-km footprint. The resulting map was then used to calibrate and subsequently classify crop residue mapping using Landsat imagery at a larger spatial resolution and extent. This manuscript describes how the method was applied and presents results in the form of crop-residue cover maps, validation statistics, and quantification of conservation tillage implementation in the agricultural landscape. Overall accuracy for maps derived from Landsat 7 and Landsat 8 were comparable at roughly 92% (+/− 10%). Tillage class-specific accuracy was also strong and ranged from 75% to 99%. The approach, which employed a 12-band image stack of six tillage spectral indices and six individual Landsat bands, was shown to be adaptable to variable soil-moisture conditions—under dry conditions (Landsat 7, 14 May 2015) the majority of predictive power was attributed to SWIR indices, and under wet conditions (Landsat 8, 22 May 2015) single band reflectance values were more effective at explaining variability in residue cover. Summary statistics of resulting tillage class occurrence matched closely with conservation tillage implementation totals reported by Maryland and Delaware to the Chesapeake Bay Program. This hybrid method combining WorldView-3 and Landsat imagery sources shows promise for monitoring progress in the adoption of conservation tillage practices and for describing crop-residue outcomes associated with a variety of agricultural management practices.
41
Xu, Yufen, Shangbo Zhou e Yuhui Huang. "Transformer-Based Model with Dynamic Attention Pyramid Head for Semantic Segmentation of VHR Remote Sensing Imagery". Entropy 24, n. 11 (6 novembre 2022): 1619. http://dx.doi.org/10.3390/e24111619.
Abstract (sommario):
Convolutional neural networks have long dominated semantic segmentation of very-high-resolution (VHR) remote sensing (RS) images. However, restricted by the fixed receptive field of convolution operation, convolution-based models cannot directly obtain contextual information. Meanwhile, Swin Transformer possesses great potential in modeling long-range dependencies. Nevertheless, Swin Transformer breaks images into patches that are single-dimension sequences without considering the position loss problem inside patches. Therefore, Inspired by Swin Transformer and Unet, we propose SUD-Net (Swin transformer-based Unet-like with Dynamic attention pyramid head Network), a new U-shaped architecture composed of Swin Transformer blocks and convolution layers simultaneously through a dual encoder and an upsampling decoder with a Dynamic Attention Pyramid Head (DAPH) attached to the backbone. First, we propose a dual encoder structure combining Swin Transformer blocks and reslayers in reverse order to complement global semantics with detailed representations. Second, aiming at the spatial loss problem inside each patch, we design a Multi-Path Fusion Model (MPFM) with specially devised Patch Attention (PA) to encode position information of patches and adaptively fuse features of different scales through attention mechanisms. Third, a Dynamic Attention Pyramid Head is constructed with deformable convolution to dynamically aggregate effective and important semantic information. SUD-Net achieves exceptional results on ISPRS Potsdam and Vaihingen datasets with 92.51%mF1, 86.4%mIoU, 92.98%OA, 89.49%mF1, 81.26%mIoU, and 90.95%OA, respectively.
42
Teshima, Yu, e Akira Iwasaki. "Correction of Attitude Fluctuation of Terra Spacecraft Using ASTER/SWIR Imagery With Parallax Observation". IEEE Transactions on Geoscience and Remote Sensing 46, n. 1 (gennaio 2008): 222–27. http://dx.doi.org/10.1109/tgrs.2007.907424.
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Hu, Yue, Zhuna Wang, Yahao Zhang e Yuanyong Dian. "Logging Pattern Detection by Multispectral Remote Sensing Imagery in North Subtropical Plantation Forests". Remote Sensing 14, n. 19 (7 ottobre 2022): 4987. http://dx.doi.org/10.3390/rs14194987.
Abstract (sommario):
Forest logging detection is important for sustainable forest management. The traditional optical satellite images with visible and near-infrared bands showed the ability to identify intensive timber logging. However, less intensive logging is still difficult to detect with coarse spatial resolution such as Landsat or high spatial resolution in fewer spectral bands. Although more high-resolution remote sensing images containing richer spectral bands can be easily obtained nowadays, the questions of whether they facilitate the detection of logging patterns and which spectral bands are more effective in detecting logging patterns, especially in selective logging, remain unresolved. Therefore, this paper aims to evaluate the combinations of visible, near-infrared, red-edge, and short-wave infrared bands in detecting three different logging intensity patterns, including unlogged (control check, CK), selective logging (SL), and clear-cutting (CC), in north subtropical plantation forests with the random forest algorithm using Sentinel-2 multispectral imagery. This study aims to explore the recognition performance of different combinations of spectral bands (visual (VIS) and near-infrared bands (NIR), VIS, NIR combined with red-edge, VIS, NIR combined with short-wave infrared bands (SWIR), and full-spectrum bands combined with VIS, NIR, red edge and SWIR) and to determine the best spectral variables to be used for identifying logging patterns, especially in SL. The study was conducted in Taizishan in Hubei province, China. A total of 213 subcompartments of different logging patterns were collected and the random forest algorithm was used to classify logging patterns. The results showed that full-spectrum bands which contain the red-edge and short-wave infrared bands improve the ability of conventional optical satellites to monitor forest logging patterns and can achieve an overall accuracy of 85%, especially for SL which can achieve 79% and 64% for precision and recall accuracy, respectively. The red-edge band (698–713 nm, B5 in Sentinel-2), short-wave infrared band (2100–2280 nm, B12 in Sentinel-2), and associated vegetation indices (NBR, NDre2, and NDre1) enhance the sensitivity of the spectral information to logging patterns, especially for the SL pattern, and the precision and recall accuracy can improve by 10% and 6%, respectively. Meanwhile, both clear-cutting and unlogged patterns could be well-classified whether adding a red-edge or SWIR band or both in VIS and NIR bands; the best precision and recall accuracies for clear-cutting were enhanced to 97%, 95% and 81%, 91% for unlogged, respectively. Our results demonstrate that the optical images have the potential ability to detect logging patterns especially for the clear-cutting and unlogged patterns, and the selective logging detection accuracy can be improved by adding red-edge and short-wave infrared spectral bands.
44
Ryu, Han-Sol, e Sungwook Hong. "Sea Fog Detection Based on Normalized Difference Snow Index Using Advanced Himawari Imager Observations". Remote Sensing 12, n. 9 (10 maggio 2020): 1521. http://dx.doi.org/10.3390/rs12091521.
Abstract (sommario):
Many previous studies have attempted to distinguish fog from clouds using low-orbit and geostationary satellite observations from visible (VIS) to longwave infrared (LWIR) bands. However, clouds and fog have often been misidentified because of their similar spectral features. Recently, advanced meteorological geostationary satellites with improved spectral, spatial, and temporal resolutions, including Himawari-8/9, GOES-16/17, and GeoKompsat-2A, have become operational. Accordingly, this study presents an improved algorithm for detecting daytime sea fog using one VIS and one near-infrared (NIR) band of the Advanced Himawari Imager (AHI) of the Himawari-8 satellite. We propose a regression-based relationship for sea fog detection using a combination of the Normalized Difference Snow Index (NDSI) and reflectance at the green band of the AHI. Several case studies, including various foggy and cloudy weather conditions in the Yellow Sea for three years (2017–2019), have been performed. The results of our algorithm showed a successful detection of sea fog without any cloud mask information. The pixel-level comparison results with the sea fog detection based on the shortwave infrared (SWIR) band (3.9 μm) and the brightness temperature difference between SWIR and LWIR bands of the AHI showed high statistical scores for probability of detection (POD), post agreement (PAG), critical success index (CSI), and Heidke skill score (HSS). Consequently, the proposed algorithms for daytime sea fog detection can be effective in daytime, particularly twilight, conditions, for many satellites equipped with VIS and NIR bands.
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Lin, Yukun, Zhe Zhu, Wenxuan Guo, Yazhou Sun, Xiaoyuan Yang e Valeriy Kovalskyy. "Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery". Remote Sensing 12, n. 7 (6 aprile 2020): 1176. http://dx.doi.org/10.3390/rs12071176.
Abstract (sommario):
Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R2 = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R2 = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred.
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Báčová, M., e J. Krása. "Application of historical and recent aerial imagery in monitoring water erosion occurrences in Czech highlands". Soil and Water Research 11, No. 4 (12 ottobre 2016): 267–76. http://dx.doi.org/10.17221/178/2015-swr.
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Neri, M. P., A. B. Baloloy e A. C. Blanco. "LIMITATION ASSESSMENT AND WORKFLOW REFINEMENT OF THE MANGROVE VEGETATION INDEX (MVI)-BASED MAPPING METHODOLOGY USING SENTINEL-2 IMAGERY". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W6-2021 (18 novembre 2021): 235–42. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w6-2021-235-2021.
Abstract (sommario):
Abstract. The Mangrove Vegetation Index (MVI) was developed to map mangroves extent from remotely-sensed imageries accurately and quickly. MVI measures the probability of a pixel to be a ‘mangrove’ by extracting the greenness and moisture information from the green, NIR, and SWIR bands. The range of MVI values may vary depending on factors such as land cover classes, climatic conditions, or tidal conditions. Mapping the scope of mangrove sites involves setting a maximum and minimum MVI threshold to separate them from other land cover classes and vegetation. Although the MVI has a high index accuracy, its mapping performance is limited by some biophysical and environmental factors. Misclassification occurs in aquacultural areas, irrigated croplands, and sites with palm trees where mangrove and surrounding vegetation pixels have highly similar spectral signatures. There are scenes with complex environments, such as in aquaculture areas and along a network of rivers and streams, where an optimal threshold varies across the site, and setting a single MVI threshold may not yield excellent results. An automated threshold setting using the Otsu method was explored; however, the results were inaccurate due to a low intensity contrast between mangroves and other vegetation in the MVI raster layer. This study also looked into possible adjustments to improve the manual threshold setting workflow for a successful mapping of mangrove extent using MVI on Sentinel-2 imagery.
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Bouguerra, Sana, Sihem Jebari e Jamila Tarhouni. "Spatiotemporal analysis of landscape patterns and its effect on soil loss in the Rmel river basin, Tunisia". Soil and Water Research 16, No. 1 (11 dicembre 2020): 39–49. http://dx.doi.org/10.17221/84/2019-swr.
Abstract (sommario):
Changes in land use and land cover (LULC) are generally associated with environment pollution and the degradation of natural resources. Detecting LULC changes is essential to assess the impact on ecosystem services. The current research studies the impact of the LULC change on the soil loss and sediment export in a period of 43 years from 1972 to 2015. Landsat imageries were classified into five classes using a supervised classification method and the maximum likelihood Algorithm. Then, the sediment retention service for avoiding reservoir sedimentation was assessed using the InVEST SDR (integrated valuation of ecosystem services and trade-offs sediment delivery ratio) model. The results showed that the changes are very important in this study period (1972–2015). Forests were reduced by 18.72% and croplands were increased by approximately 54%. The InVEST SDR model simulation results reveal an increase in the sediment export and soil loss, respectively, from 1.68 to 5.57 t/ha/year and from 15.22 to 43.61 t/ha/year from the year 1972 to 2015. These results highlight the need for targeted policies on integrated land and water resource management. Then, it is important to improve the common understanding of land use and land cover dynamics to the different stakeholders. All these can help in projecting future changes in the LULC and to investigate more appropriate policy interventions for achieving better land and water management.
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Hao, Mengmeng, Xiaohan Dong, Dong Jiang, Xianwen Yu, Fangyu Ding e Jun Zhuo. "Land-use classification based on high-resolution remote sensing imagery and deep learning models". PLOS ONE 19, n. 4 (18 aprile 2024): e0300473. http://dx.doi.org/10.1371/journal.pone.0300473.
Abstract (sommario):
High-resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced. However, there has been a lack of a clear understanding of the performance of these models. In this study, we applied four well-established and robust deep learning models (FCN-8s, SegNet, U-Net, and Swin-UNet) to an open benchmark high-resolution remote sensing dataset to compare their performance in land-use mapping. The results indicate that FCN-8s, SegNet, U-Net, and Swin-UNet achieved overall accuracies of 80.73%, 89.86%, 91.90%, and 96.01%, respectively, on the test set. Furthermore, we assessed the generalization ability of these models using two measures: intersection of union and F1 score, which highlight Swin-UNet’s superior robustness compared to the other three models. In summary, our study provides a systematic analysis of the classification differences among these four deep learning models through experiments. It serves as a valuable reference for selecting models in future research, particularly in scenarios such as land-use mapping, urban functional area recognition, and natural resource management.
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Lee, Hwa-Seon, e Kyu-Sung Lee. "A MULTI-TEMPORAL APPROACH FOR DETECTING SNOW COVER AREA USING GEOSTATIONARY IMAGERY DATA". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (23 giugno 2016): 511–12. http://dx.doi.org/10.5194/isprs-archives-xli-b8-511-2016.
Abstract (sommario):
In this study, we attempt to detect snow cover area using multi-temporal geostationary satellite imagery based on the difference of spectral and temporal characteristics between snow and clouds. The snow detection method is based on sequential processing of simple thresholds on multi-temporal GOCI data. We initially applied a simple threshold of blue reflectance and then root mean square deviation (RMSD) threshold of near infrared (NIR) reflectance that were calculated from time-series GOCI data. Snow cover detected by the proposed method was compared with the MODIS snow products. The proposed snow detection method provided very similar results with the MODIS cloud products. Although the GOCI data do not have shortwave infrared (SWIR) band, which can spectrally separate snow cover from clouds, the high temporal resolution of the GOCI was effective for analysing the temporal variations between snow and clouds.