EMS-Net: Efficient Multi-Temporal Self-Attention For Hyperspectral Change Detection
This addresses precise fine object evolution monitoring in urban development, but appears incremental as it builds on existing self-attention and contrastive learning methods.
The paper tackled hyperspectral change detection by proposing EMS-Net, which uses an efficient multi-temporal self-attention module and a supervised contrastive loss, achieving outstanding performance on two datasets.
Hyperspectral change detection plays an essential role of monitoring the dynamic urban development and detecting precise fine object evolution and alteration. In this paper, we have proposed an original Efficient Multi-temporal Self-attention Network (EMS-Net) for hyperspectral change detection. The designed EMS module cuts redundancy of those similar and containing-no-changes feature maps, computing efficient multi-temporal change information for precise binary change map. Besides, to explore the clustering characteristics of the change detection, a novel supervised contrastive loss is provided to enhance the compactness of the unchanged. Experiments implemented on two hyperspectral change detection datasets manifests the out-standing performance and validity of proposed method.