Titlebook: Dimensionality Reduction of Hyperspectral Imagery; Arati Paul,Nabendu Chaki Book 2024 The Editor(s) (if applicable) and The Author(s), und

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善辯

Dimensionality Reduction: State of the Art,most discriminating characteristics, and therefore, the physical relevance of the selected bands is maintained. This chapter discusses the state-of-the-art methods of dimensionality reduction of HSI. Specific gap areas are also analysed, and accordingly, improved methodologies are given in subsequen

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準(zhǔn)則

Data-Driven Approach for Hyperspectral Band Selection,en band selection (BS) approach employs multi-featured analysis and signal-to-noise-ratio (SNR)-based band prioritisation for selecting discriminating bands. The signal quantisation process is used in the supervised data-driven approach for distinctly identifying each class signature pattern using a

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Concluding Remarks and Way Forward,ion time. The effect of noise is also analysed for optimisation and ranking-based band selection (BS) methods. The data-driven approaches for band selection show significant advantage as they do not depend on user perception to select the required number of discriminating bands from the data. At the

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Commonwealth

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Jochen Seemann,Jürgen Wolff von Gudenbergmost discriminating characteristics, and therefore, the physical relevance of the selected bands is maintained. This chapter discusses the state-of-the-art methods of dimensionality reduction of HSI. Specific gap areas are also analysed, and accordingly, improved methodologies are given in subsequen

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