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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Zusammenfassende Darstellung der Ergebnisse, optimisation-based band selection approaches are discussed using genetic algorithms (GAs) and particle swam optimisation (PSO). In contrast to exhaustive search algorithms, optimisation-based approach employs fast search measures to find a better solution in a large solution space. The key strength

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https://doi.org/10.1007/978-3-658-15902-3agery (HSI) to improve classification accuracy. In most of the DR methods, the required number of selected/extracted bands is given by the user. However, in reality, it is difficult to perceive the required number of bands before the analysis starts. A particular number of (selected or extracted) fe

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Book 2024. The authors first explain how hyperspectral imagery (HSI) plays an important role in remote sensing due to its high spectral resolution that enables better identification of different materials on the earth’s surface. The authors go on to describe potential challenges due to HSI being acquired in

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Jochen Seemann,Jürgen Wolff Gudenbergnsidered similar, and the one with higher variance is accepted as being more discriminating. Finally, the selected bands are classified, and overall accuracy (OA) is calculated. This method is compared with four other existing state-of-the-art methods in the similar field in terms of OA and execution time for evaluating the performance.