The BD-MSA model aims to improve change detection accuracy by aggregating global and local feature information while separating the main body from the edge of the changing region. The model outperforms existing methods on datasets like DSIFN-CD, S2Looking, and WHU-CD.
The content discusses the importance of remote sensing image change detection (RSCD) for various applications like urban planning and disaster assessment. It highlights challenges faced by current RSCD algorithms due to factors like shooting angles and lighting conditions.
Deep learning techniques have shown promise in RSCD tasks, with models categorized based on their structure as convolution-based, attention mechanism-based, or Transformer-based. The proposed BD-MSA model combines features like OFAM for multi-scale information aggregation and MixFFN for improved feature representation.
Experimental results demonstrate that BD-MSA achieves state-of-the-art performance on different datasets compared to other models. Ablation studies confirm the impact of modules like OFAM and MixFFN on enhancing F1 scores and IoU metrics in change detection tasks.
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by Yonghui Tan,... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2401.04330.pdfDeeper Inquiries