![]() ![]() We found that PCA, KPCA, and ICA post greater signal reconstruction capability however, when compression rates are more than 90% these methods show lower classification scores. urban, transitional suburban, and forests) collected by the Remote Sensing and Spatial Ecosystem Modeling laboratory of the University of Toronto. We use three high-resolution hyperspectral image datasets, representing three common landscape types (i.e. Pixel classification accuracies together with compression method, compression rates, and reconstruction errors provide a new lens to study the suitability of a compression method for the task of pixel classification. Compressed pixels are subsequently used to perform pixel classifications. We use five dimensionality reduction methods-PCA, KPCA, ICA, AE, and DAE-to compress 301-dimensional hyperspectral pixels. ![]() ![]() This paper presents a systematic study of the effects of hyperspectral pixel dimensionality reduction on the pixel classification task. ![]()
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