Results 11 to 20 of about 9,036 (210)
Orthogonal Subspace Projection Target Detector for Hyperspectral Anomaly Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Orthogonal subspace projection (OSP) is a versatile hyperspectral imaging technique which has shown great potential in dimensionality reduction, target detection, spectral unmixing, etc. However, due to its inherent requirement of prior target knowledge,
Chein-I Chang, Hongju Cao, Meiping Song
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SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022 [PDF]
Earth System Science Data, 2022 The drawbacks of low-coverage rate in global land inevitably exist in satellite-based daily soil moisture products because of the satellite orbit covering scopes and the limitations of soil moisture retrieving models.
Q. Zhang +4 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Target extraction can provide a prior knowledge for spectral unmixing, unsupervised hyperspectral image classification, and unsupervised target detection tasks, which is of great practice.
Xiaodi Shang +4 more
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IET Image Processing, 2022 Although hyperspectral data, especially spaceborne images, are rich in spectral information, their spatial resolution is usually low due to the limitation of sensor design and other factors.
Haoyang Yu +5 more
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A HYPERSPECTRAL REMOTE SENSING FUSION TECHNOLOGY BASED ON SPECTRAL NORMALIZATION OF GF AND ZY SERIES SATELLITES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Globalized surface coverage, environmental monitoring and other earth system science and high-quality global surface coverage monitoring applications urgently need basic hyperspectral remote sensing reflectance data to support.
S. Liu +5 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Convolutional neural networks (CNN) have led to a successful breakthrough for hyperspectral image classification (HSIC). Due to the intrinsic spatial-spectral specificities of a hyperspectral cube, feature extraction with 3-D convolution operation is a ...
Chunyan Yu +4 more
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Editorial for Special Issue “Advances in Hyperspectral Data Exploitation”
Remote Sensing, 2022 Hyperspectral imaging (HSI) has emerged as a promising, advanced technology in remote sensing and has demonstrated great potential in the exploitation of a wide variety of data.
Chein-I Chang +8 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Band redundancy and limitation of labeled samples restrict the development of hyperspectral image classification (HSIC) greatly. To address the earlier issues, the classification models such as subspace-based support vector machines, which have gained a ...
Jiaochan Hu +5 more
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Utilizing Hyperspectral Remote Sensing for Soil Gradation [PDF]
Remote Sensing, 2020 Soil gradation is an important characteristic for soil mechanics. Traditionally soil gradation is performed by sieve analysis using a sample from the field. In this research, we are interested in the application of hyperspectral remote sensing to characterize soil gradation.
Jordan Ewing +3 more
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IEEE Access, 2023 Since hyperspectral remote sensing images are three-dimensional data cubes with spatial and spectral information, with many wavebands and high inter-band correlation, the number of training samples required for classification is greatly increased.
Chaozhu Zhang +3 more
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