The effects of spectral dimensionality reduction on hyperspectral pixel classification: A case study.

ABSTRACT: This paper presents a systematic study of the effects of hyperspectral pixel dimensionality reduction on the pixel classification task. We use five dimensionality reduction methods-PCA, KPCA, ICA, AE, and DAE-to compress 301-dimensional hyperspectral pixels. Compressed pixels are subsequently used to perform pixel classifications. 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. We use three high-resolution hyperspectral image datasets, representing three common landscape types (i.e. urban, transitional suburban, and forests) collected by the Remote Sensing and Spatial Ecosystem Modeling laboratory of the University of Toron

SUBMITTER: Mantripragada K 

PROVIDER: S-EPMC9282587 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature