Spectral Unmixing of Hyperspectral Remote Sensing Imagery via Preserving the Intrinsic Structure Invariant
نویسندگان
چکیده
منابع مشابه
Overlap-based feature weighting: The feature extraction of Hyperspectral remote sensing imagery
Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based feature weigh...
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Ruyi Feng 1 ID , Yanfei Zhong 2,* ID , Lizhe Wang 1,* and Wenjuan Lin 3,* 1 School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China; [email protected] 2 State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China 3 School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, C...
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Hyperspectral remote sensing imagery contains much more information in the spectral domain than does multispectral imagery. The consecutive and abundant spectral signals provide a great potential for classification and anomaly detection. In this study, two real hyperspectral data sets were used for anomaly detection. One data set was an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) da...
متن کاملoverlap-based feature weighting: the feature extraction of hyperspectral remote sensing imagery
hyperspectral sensors provide a large number of spectral bands. this massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. we propose to use overlap-based feature weigh...
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ژورنال
عنوان ژورنال: Sensors
سال: 2018
ISSN: 1424-8220
DOI: 10.3390/s18103528