BiFA-Net: Bidirectional Focused Attention Network for Hyperspectral Image Change Detection
Year: 2026
Type: article
Abstract: Hyperspectral image (HSI) change detection faces significant challenges in balancing the high computational complexity of standard Transformers with the need to capture both long-range spatiotemporal interactions and fine-grained local details. To overcome these limitations, this paper proposes a Bidirectional Focused Attention Network (BiFA-Net) that employs a dual-branch architecture consisting of a change-aware interaction branch and a Global Semantic Difference Encoder (GSDE) branch. In the interaction branch, we design a Change-Aware Focused Linear Attention (CA-FLA) module, which introduces a global difference descriptor to dynamically generate head-specific focusing and scaling factors. This mechanism enables the adaptive re-weighting of the attention distribution and utilizes bidirectional cross-attention to enhance the alignment of bitemporal features. Simultaneously, a parallel difference branch directly extracts multi-scale local spatial-spectral patterns from the difference image. The interaction discrepancy representation generated by CA-FLA and the semantic difference representation extracted by GSDE are further refined using separate Long Short-Term Memory (LSTM) networks before feature fusion. On the River, Farmland, and Hermiston datasets, BiFA-Net achieves overall accuracies of 96.69%, 97.41%, and 98.97%, respectively, showing clear advantages over existing methods in both accuracy and robustness.
Source: Sensors
DOI: 10.3390/s26227096
Institutions: Hunan Institute of Science and Technology, Sichuan University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225745516