Hyperspectral Image Change Detection Based on Gated Spectral–Spatial–Temporal Attention Network With Spectral Similarity Filtering

Hyperspectral imaging enables advanced change detection (CD) but struggles with extensive redundant data across spatial and spectral dimensions. This bloats model size and computational loads. To address this problem, we propose a new gated spectral–spatial–temporal attention network with spectral similarity filtering (HyGSTAN) with a lightweight yet accurate architectural design. Specifically, our HyGSTAN introduces three innovative modules: 1) spectral similarity filtering to reduce spectral redundancy via cosine similarity; 2) gated spectral–spatial attention to capture intra-image spatial features using single-head weak self-attention and gated mechanisms; and 3) gated spectral–spatial–temporal attention to extract inter-image temporal changes. Experiments on three benchmark datasets demonstrate HyGSTAN’s ability to balance accuracy, model complexity, and computational efficiency. The proposed attention mechanisms extract more discriminative information without sacrificing performance. The source code of this work will be released at https://github.com/Welcome-to-LISA/HyGSTAN.

• Publication year

  2024

• Venue

  IEEE Transactions on Geoscience and Remote Sensing

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1109/TGRS.2024.3373820
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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