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 also presented in detail.

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https://doi.org/10.1007/978-3-030-43865-4cularly for Earth Observation (EO). Space-borne and airborne platforms equipped with powerful sensors make it possible to acquire detailed information from the surface of the earth. Hyperspectral imaging sensors have the capability of capturing the detailed spectral characteristics of the received light in the sensor’s covered area.

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Book 2021ed 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 professio

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2662-3366 and-noise factor-based formulation.Presents unsupervised spe.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 c

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Challenges and Future Prospects,xtracting and analyzing all the rich spectral and spatial materials enclosed in the hyperspectral image. Secondly, the very complex data is the integration of spectral and spatial information with the Hughes phenomenon, very limited labeled samples, and redundancy with inherent sensor and environmental noise.

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