Comparison and Evaluation of TV-Based and Low-Rank-Based Destriping Algorithms for Hyperspectral Imagery

Abstract

Remote sensing image data especially hyperspectral imagery (HSI) data has been widely applied in various research fields. However, hyperspectral imagery often suffers from significant noise contamination, such as stripe noise and Gaussian noise. These noises adversely affect the utilization of remote sensing data, prompting the development of numerous noise removal algorithms for hyperspectral imagery. Specifically, addressing stripe noise involves employing different strategies, including the employment of total-variation-based (TV-based) and low-rank-based algorithms. Yet, evaluating the effectiveness and applicability of these algorithms can be challenging due to variations in testing conditions provided by their respective authors during the proposal phase. Consequently, our aim was to offer a comprehensive and impartial evaluation of these stripe noise removal algorithms for hyperspectral imagery. Within the TV-based and low-rank-based algorithm categories, we had chosen ten distinct algorithms on which to conduct third-party testing and evaluation. We had considered various noise scenarios, resulting in a total of 48 noise configurations. Our evaluation encompassed multiple dimensions, including the quality of noise removal, computational speed, and parameter stability, thereby delivering a comprehensive assessment of the performance of different stripe noise removal algorithms. Additionally, we had conducted an analysis to identify the underlying causes of varying denoising results across different algorithms. This analysis offered valuable insights and recommendations for the future development of denoising algorithms.