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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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發(fā)表于 2025-3-21 18:00:31 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Deep Learning for Hyperspectral Image Analysis and Classification
編輯Linmi Tao,Atif Mughees
視頻videohttp://file.papertrans.cn/265/264609/264609.mp4
概述Proposes adaptive-boundary adjustment-based noise detection and group-wise band categorization with unsupervised spectral-spatial adaptive band-noise factor-based formulation.Presents unsupervised spe
叢書名稱Engineering Applications of Computational Methods
圖書封面Titlebook: Deep Learning for Hyperspectral Image Analysis and Classification;  Linmi Tao,Atif Mughees Book 2021 The Editor(s) (if applicable) and The
描述.This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly...This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are theoriginal contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends..
出版日期Book 2021
關鍵詞Remote sensing; Hyperspectral image analysis; Deep learning; Stacked auto-encoder; Deep belief network; S
版次1
doihttps://doi.org/10.1007/978-981-33-4420-4
isbn_softcover978-981-33-4422-8
isbn_ebook978-981-33-4420-4Series ISSN 2662-3366 Series E-ISSN 2662-3374
issn_series 2662-3366
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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Hyperspectral Image Spatial Feature Extraction via Segmentation,tion 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 also presented in detail.
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Sparse-Based Hyperspectral Data Classification,In this section, we restate the sparsest solution problem using a geometric interpretation. Finding the sparsest solution is strictly equivalent to the .-norm problem in Eq.?.. Unfortunately, this .-minimization problem is computationally intensive, so we will prove that the following .-minimization approach in Eq.?. is a good approximation to it.
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