SSA-LHCD: a singular spectrum analysis-driven lightweight network with 2-D self-attention for hyperspectral change detection.

Li, Yinhe; Ren, Jinchang; Yan, Yijun; Sun, Genyun; Ma, Ping

Abstract

As an emerging research hotspot in contemporary remote sensing, hyperspectral change detection (HCD) has attracted increasing attention in remote sensing Earth observation, covering land mapping changes and anomaly detection. This is primarily attributable to the unique capacity of hyperspectral imagery (HSI) to amalgamate both the spectral and spatial information in the scene, facilitating a more exhaustive analysis and change detection on the Earth's surface, proving to be successful across diverse domains, such as disaster monitoring and geological surveys. Although numerous HCD algorithms have been developed, most of them face three major challenges: (i) susceptibility to inherent data noise, (ii) inconsistent accuracy of detection, especially when dealing with multi-scale changes, and (iii) extensive hyperparameters and high computational costs. As such, we propose a singular spectrum analysis-driven-lightweight network for HCD, where three crucial components are incorporated to tackle these challenges. Firstly, singular spectrum analysis (SSA) is applied to alleviate the effect of noise. Next, a 2-D self-attention-based spatial–spectral feature-extraction module is employed to effectively handle multi-scale changes. Finally, a residual block-based module is designed to effectively extract the spectral features for efficiency. Comprehensive experiments on three publicly available datasets have fully validated the superiority of the proposed SSA-LHCD model over eight state-of-the-art HCD approaches, including four deep learning models.

Citation

LI, Y., REN, J., YAN, Y., SUN, G. and MA, P. 2024. SSA-LHCD: a singular spectrum analysis-driven lightweight network with 2-D self-attention for hyperspectral change detection. Remote sensing [online], 16(3), article number 2353. Available from: https://doi.org/10.3390/rs16132353

• Journal Article Type: Article
• Acceptance Date: Jun 26, 2024
• Online Publication Date: Jun 27, 2024
• Publication Date: Jul 1, 2024
• Deposit Date: Jul 2, 2024
• Publicly Available Date: Jul 2, 2024
• Journal: Remote sensing
• Electronic ISSN: 2072-4292
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 13
• Article Number: 2353
• DOI: https://doi.org/10.3390/rs16132353
• Keywords: Hyperspectral imagery (HSI); Hyperspectral change detection (HCD); Deep learning; Singular spectral analysis (SSA); Residual block
• Public URL: https://rgu-repository.worktribe.com/output/2383618