Pytorch implementation of ISPRS paper "GlobalMind: Global multi-head interactive self-attention network for hyperspectral change detection".
Abstract: High spectral resolution imagery of the Earth’s surface enables users to monitor changes over time in fine- grained scale, playing an increasingly important role in agriculture, defense, and emergency response. However, most current algorithms are still confined to describing local features and fail to incorporate a global perspective, which limits their ability to capture interactions between global features, thus usually resulting in incomplete change regions. In this paper, we proposed a Global Multi-head INteractive selfattention change Detection network (GlobalMind) to explore the implicit correlation between different surface objects and variant land cover transformations, acquiring a comprehensive understanding of the data and accurate change detection result. Firstly, a simple but effective Global Axial Segmentation (GAS) is designed to expand the self-attention computation along the row space or column space of hyperspectral images, allowing the global connection with high efficiency. Secondly, with GAS, the global spatial multi-head interactive self-attention (GlobalM) module is crafted to mine the abundant spatial-spectral feature involving potential correlations between the ground objects from the entire rich and complex hyperspectral space. Moreover, to acquire the accurate and complete crosstemporal changes, we devise a global temporal interactive multi-head self-attention (GlobalD) module which incorporates the relevance and variation of bi-temporal spatial-spectral features, deriving the integrate potential same kind of changes in the local and global range with the combination of GAS. A new and challenging hyperspectral change detection dataset is designed for comparison of different approaches. We perform extensive experiments on six real hyperspectral datasets, and our method outperforms the state-of-the-art algorithms with high accuracy and efficiency.
GlobalMind: Global multi-head interactive self-attention network for hyperspectral change detection
We perform extensive experiments on six real hyperspectral datasets: 1) Farmland, 2) Hermiston, 3) River, 4) Bay, 5) Barbara, 6) GF5B_BI.
All six hyperspectral binary change detection datasets are provided here [BaiduCloud]:https://pan.baidu.com/s/1-pw4jrd-xp7QHPS067mV7g?pwd=aey7 (code=aey7).
The binary change detection results of propose GlobalMIND are available at: [BaiduCloud]: https://pan.baidu.com/s/1bgOzlOTMeu0jCTJkAl42ng?pwd=ny2i (code= ny2i)
Please cite our paper if you find it useful for your research. @article{hu2024globalmind, title={GlobalMind: Global multi-head interactive self-attention network for hyperspectral change detection}, author={Hu, Meiqi and Wu, Chen and Zhang, Liangpei}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={211}, pages={465--483}, year={2024}, publisher={Elsevier} }