The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study

TL;DR

This study systematically evaluates how different spectral dimensionality reduction techniques impact hyperspectral pixel classification accuracy across various datasets, highlighting the importance of method choice and compression rate.

Contribution

It provides a comprehensive comparison of five dimensionality reduction methods and their effects on classification performance at different compression levels.

Findings

PCA, KPCA, and ICA retain more signal reconstruction capability.

AE and DAE achieve better classification accuracy at high compression rates.

Performance declines for all methods as compression exceeds 95%."

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 Toronto. 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. AE and DAE methods post better classification accuracy at 95\% compression rate, however their performance drops as compression rate approaches 97\%. Our results suggest that both the compression method and the compression rate are important considerations when designing a hyperspectral pixel classification pipeline.