Results 31 to 40 of about 2,046 (182)
Sensors, 2020 Modern satellite and aerial imagery outcomes exhibit increasingly complex types of ground objects with continuous developments and changes in land resources.
Zhanyuan Chang +3 more
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Multiview Hierarchical Network for Hyperspectral and LiDAR Data Classification [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 In recent times, multisource remote sensing technology [e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR) data] has been widely used in urban land-use recognition owing to its high classification effectiveness compared to using only single-source data.
Yishu Peng +4 more
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Remote Sensing to Characterize River Floodplain Structure and Function
Remote Sensing, 2022 Advancing understanding of the complexities and expansive spatial scales of river ecology can be enhanced through the application of remote sensing. We obtained satellite (Quickbird) and airborne (LIDAR, hyperspectral, multispectral, and thermal) imagery
F. Richard Hauer +2 more
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IEEE Access, 2018 Deep learning has been widely used to fuse multi-sensor data for classification. However, current deep learning architecture for multi-sensor data fusion might not always perform better than single data source, especially for the fusion of hyperspectral ...
Wenzhi Liao +5 more
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FUSION OF HYPERSPECTRAL, MULTISPECTRAL, COLOR AND 3D POINT CLOUD INFORMATION FOR THE SEMANTIC INTERPRETATION OF URBAN ENVIRONMENTS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 In this paper, we address the semantic interpretation of urban environments on the basis of multi-modal data in the form of RGB color imagery, hyperspectral data and LiDAR data acquired from aerial sensor platforms.
M. Weinmann, M. Weinmann
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Remote Sensing, 2021 Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction.
Caileigh Shoot +5 more
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LiDAR-Guided Reduction Of Spectral Variability in Hyperspectral Imagery
2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2019 Hyperspectral unmixing has attained a great importance in recent decades in remote sensing applications. Due to some external effect (illumination conditions) or internal effects (concentration of chlorophyll), spectral variability exists in hyperspectral images. This spectral variability causes significant errors in abundance estimates. In this paper,
Kahraman, Sevcan +4 more
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Remote Sensing, 2019 In recent years, the outbreak of the pine shoot beetle (PSB), Tomicus spp., has caused serious shoots damage and the death of millions of trees in Yunnan pine forests in southwestern China.
Qinan Lin +4 more
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Semantic segmentation of hyperspectral images with the fusion of LiDAR data
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016 Semantic segmentation is an emerging field in the computer vision community where one can segment and label an object all at once. In this paper, we propose a semantic segmentation algorithm that takes into account both the hyperspectral images and the LiDAR data.
Aytaylan, Hakan, Yuksel, Seniha Esen
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Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014 The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets
Christian Debes +13 more
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