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,on accuracies and improved the speed of convergence. The reduction of spatial dimension also contributed in improving the computation time. The presented methods also reduced the effect of noise in hyperspectral imagery for efficient band selection.

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978-3-031-42669-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Jochen Seemann,Jürgen Wolff von Gudenbergd the associated dataset, this chapter introduces the background of remote sensing (RS), which includes a brief discussion on electromagnetic spectrum, atmospheric transmission window, atmospheric scattering, surface reflection, reflectance curve and RS data characteristics. This chapter also includ

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https://doi.org/10.1007/3-540-30950-0e image. Hence, dimensionality reduction is applied as an essential pre-processing step in hyperspectral data analysis. Pooling is a technique of reducing spatial dimension and is successfully applied in intermediate layers of convolutional neural networks for spatial feature extraction. There are v

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Jochen Seemann,Jürgen Wolff Gudenbergr, a minimum redundancy– and maximum variance–based unsupervised band selection method is presented. Since ranking-based band selection methods are iterative in nature, the huge spatial dimension of the hyperspectral image increases the computation time of the dimensionality reduction (DR) method. H