On Hyperspectral Classification in the Compressed Domain

TL;DR

This paper explores hyperspectral pixel classification directly in the compressed domain, enabling real-time processing and improved accuracy through diverse measurement matrices, without reconstructing the full data cube.

Contribution

It introduces a novel approach for hyperspectral classification in the compressed domain, emphasizing the benefits of using distinct measurement matrices for enhanced accuracy.

Findings

Using different measurement matrices improves classification accuracy.

Classification in the compressed domain supports real-time processing.

Diversity in measurement matrices enhances learning performance.

Abstract

In this paper, we study the problem of hyperspectral pixel classification based on the recently proposed architectures for compressive whisk-broom hyperspectral imagers without the need to reconstruct the complete data cube. A clear advantage of classification in the compressed domain is its suitability for real-time on-site processing of the sensed data. Moreover, it is assumed that the training process also takes place in the compressed domain, thus, isolating the classification unit from the recovery unit at the receiver's side. We show that, perhaps surprisingly, using distinct measurement matrices for different pixels results in more accuracy of the learned classifier and consistent classification performance, supporting the role of information diversity in learning.