Interactive Siamese spatial-Spectral cross-layer fusion transformer for hyperspectral image change detection

ABSTRACT

Recently, methods based on Transformer have been widely used in the research field of hyperspectral image (HSI) change detection (CD). However, existing transformer-based CD research does not sufficiently utilize the spatial-spectral features of HSIs. In this article, we propose an interactive Siamese spatial-spectral cross-layer fusion Transformer (IS2CF-Former) network to improve the accuracy of HSI-CD. The proposed Siamese interactive module integrates the Siamese network with the Transformer structure, enhancing communication between bi-temporal images. We have made improvements to the cross-layer adaptive fusion (CAF) Transformer, where the cross-layer fusion module enhances the interaction between layers and the ability to capture local contextual features, concurrently reducing the model’s parameter count to mitigate the risk of overfitting. The CAF Transformer is applied to extract spatial and spectral features. Evaluating the detection performance of the proposed model on three bi-temporal HSIs through extensive experiments demonstrates superior accuracy compared to seven excellent CD methods.

KEYWORDS:

• Hyper Spectral Image (HSI)
• Change Detection (CD)
• transformer
• siamese network
• cross-layer Adaptive Fuse (CAF)

Acknowledgements

We would like to thank the Remote Sensing Laboratory, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, for providing the Farmland data set, and the CiTIUS for providing the Hermiston data set.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62071168. in part by the Natural Science Foundation of Jiangsu Province under Grant [BK20211201], in part by the China Postdoctoral Science Foundation under Grant [2021M690885], and in part by the National Natural Science Foundation of China under Grant [52069014].