Segmented and Non-Segmented Stacked Denoising Autoencoder for Hyperspectral Band Reduction

TL;DR

This paper introduces an unsupervised stacked denoising autoencoder approach for hyperspectral band reduction, effectively preserving data structure while reducing dimensionality, and demonstrating improved efficiency and accuracy in classification and clustering tasks.

Contribution

It proposes a novel unsupervised autoencoder-based band reduction method that processes hyperspectral data in smaller regions for better efficiency and structure preservation.

Findings

Outperforms state-of-the-art band reduction methods in experiments

Improves classification and clustering accuracy on hyperspectral datasets

Reduces computational complexity of hyperspectral data processing

Abstract

Hyperspectral image analysis often requires selecting the most informative bands instead of processing the whole data without losing the key information. Existing band reduction (BR) methods have the capability to reveal the nonlinear properties exhibited in the data but at the expense of loosing its original representation. To cope with the said issue, an unsupervised non-linear segmented and non-segmented stacked denoising autoencoder (UDAE) based BR method is proposed. Our aim is to find an optimal mapping and construct a lower-dimensional space that has a similar structure to the original data with least reconstruction error. The proposed method first confronts the original hyperspectral data into smaller regions in a spatial domain and then each region is processed by UDAE individually. This results in reduced complexity and improved efficiency of BR for both semi-supervised and unsupervised tasks, i.e. classification and clustering. Our experiments on publicly available hyperspectral datasets with various types of classifiers demonstrate the effectiveness of UDAE method which equates favorably with other state-of-the-art dimensionality reduction and BR methods.