Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse Representations

TL;DR

This paper introduces RhyDe, a novel hyperspectral image denoising method that leverages low-rank and sparse representations to improve noise removal and rare pixel detection in high-resolution spectral data.

Contribution

The paper presents RhyDe, a new hyperspectral denoising approach that explicitly models low-rank structures and promotes sparsity to enhance denoising and anomaly detection.

Findings

RhyDe effectively denoises hyperspectral images with high accuracy.

The method preserves rare pixels better than existing techniques.

Experimental results show improved detection of subtle spectral features.

Abstract

Hyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels. This enables objects to be detected and the identification of materials that have subtle differences between them. However, the increase in spectral resolution often means that there is a decrease in the number of photons received in each channel, which means that the noise linked to the image formation process is greater. This degradation limits the quality of the extracted information and its potential applications. Thus, denoising is a fundamental problem in hyperspectral image (HSI) processing. As images of natural scenes with highly correlated spectral channels, HSIs are characterized by a high level of self-similarity and can be well approximated by low-rank representations. These characteristics underlie the state-of-the-art methods used in HSI denoising. However, where there are rarely occurring pixel types, the denoising performance of these methods is not optimal, and the subsequent detection of these pixels may be compromised. To address these hurdles, in this article, we introduce RhyDe (Robust hyperspectral Denoising), a powerful HSI denoiser, which implements explicit low-rank representation, promotes self-similarity, and, by using a form of collaborative sparsity, preserves rare pixels. The denoising and detection effectiveness of the proposed robust HSI denoiser is illustrated using semireal and real data.