EMS-Net: Efficient Multi-Temporal Self-Attention For Hyperspectral Change Detection

Meiqi Hu; Chen Wu; Bo Du

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arXiv · 2303.13753

EMS-Net: Efficient Multi-Temporal Self-Attention For Hyperspectral Change Detection

Meiqi Hu·Chen Wu·Bo Du

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Abstract

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.

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BibTeX RIS

Meiqi Hu, Chen Wu, Bo Du. 2023-03-24. EMS-Net: Efficient Multi-Temporal Self-Attention For Hyperspectral Change Detection. https://arxiv.org/abs/2303.13753

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