EMS-Net: Efficient Multi-Temporal Self-Attention For Hyperspectral Change Detection
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 18 references that are in the map. First posted on arXiv 24 Mar 2023.
Builds on
8 of 18 references.
References · 8
- 2017Attention Is All You Needcited by 59,932
- 2021Remote Sensing Image Change Detection with Transformerscited by 305
- 2022Fully Convolutional Change Detection Framework with Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detectioncited by 46
- 2021Binary Change Guided Hyperspectral Multiclass Change Detectioncited by 20
- 2022Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learningcited by 12
- 2020Hyperspectral Anomaly Change Detection Based on Auto-encodercited by 11
- 2022HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detectioncited by 9
- 2022Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detectioncited by 3
Built on by
Citing papers per year: 2023: 1; 2024: 1.
2 papers.