![]() This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All Hyperspectral image files are available from the Visual Computing Lab - Ontario Tech University URL. Received: JanuAccepted: Published: July 14, 2022Ĭopyright: © 2022 Mantripragada et al. ![]() Universidade Federal de Uberlandia, BRAZIL Our results suggest that both the compression method and the compression rate are important considerations when designing a hyperspectral pixel classification pipeline.Ĭitation: Mantripragada K, Dao PD, He Y, Qureshi FZ (2022) The effects of spectral dimensionality reduction on hyperspectral pixel classification: A case study. AE and DAE methods post better classification accuracy at 95% compression rate, however their performance drops as compression rate approaches 97%. 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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