Titlebook: Deep Learning for Hyperspectral Image Analysis and Classification; Linmi Tao,Atif Mughees Book 2021 The Editor(s) (if applicable) and The

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C. Ramioul,P. Tutenel,A. Heylighen as shown in Fig.?.. A complete description of all the HSI classification phases is depicted in Chap.?1, Fig.?.. This phase aims at the detection of noise and redundancy for the classification of remote sensing hyperspectral images by addressing a number of issues.

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C. Ramioul,P. Tutenel,A. Heylighention task as shown in Fig.?.. A complete description of all the HSI classification phases is depicted in Chap.?1, Fig.?.. This phase aims at the development of a novel unsupervised segmentation approach. Experimental results and comparison with the state-of-the-art existing segmentation approach are

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Lecture Notes in Computer Sciencevancements in remote sensing technology. The hyperspectral image classification involves target detection of different ground covers on the surface of the earth and the categorization of the subject’s geographical area into different classes of interest. The classification of a hyperspectral remote

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978-981-33-4422-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

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Engineering Applications of Computational Methodshttp://image.papertrans.cn/d/image/264609.jpg

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Deep Learning for Hyperspectral Image Analysis and Classification978-981-33-4420-4Series ISSN 2662-3366 Series E-ISSN 2662-3374

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Patrick Langdon,Jonathan Lazar,Hua Dongal remote sensing (HRS), also known as imaging spectroscopy, is a comparatively new technology that is presently under investigation by researchers and scientists for its vast range of applications such as target detection, minerals identification, vegetation, and identification of human structures and backgrounds.