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

arXiv: https://arxiv.org/abs/2104.00788 · HTML: https://mlsanity.com/paper/2104.00788/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 53 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)](https://mlsanity.com/paper/2003.02822/) (2020)
- [Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Norms](https://mlsanity.com/paper/1805.10232/) (2018)
- [Segmented and Non-Segmented Stacked Denoising Autoencoder for Hyperspectral Band Reduction](https://mlsanity.com/paper/1705.06920/) (2017)
- [SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery](https://mlsanity.com/paper/1806.09807/) (2018)
- [Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations](https://mlsanity.com/paper/1807.10574/) (2018)

## Built on by (most cited first, 1 of 1)

- [Adaptive Clustering for Efficient Phenotype Segmentation of UAV Hyperspectral Data](https://mlsanity.com/paper/2501.10199/) (2025)

## Abstract (arXiv)

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.