An effective unsupervised method for change detection in SAR images

Qianqian Liu; Xiaorong Xue; Kejun Liu; Haiyan Liu

doi:10.1117/12.2680149

27 June 2023 An effective unsupervised method for change detection in SAR images

Qianqian Liu, Xiaorong Xue, Kejun Liu, Haiyan Liu

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 1270508 (2023) https://doi.org/10.1117/12.2680149
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Synthetic aperture radar (SAR) image change detection has a very important and wide applications, and the complexity of SAR image data makes the accurate identification of change regions a challenging task. Many Convolutional Neural Networks (CNN) have been successfully applied to change detection tasks, but most of them have complex structures and heavy computation, so this paper proposes an unsupervised lightweight Convolutional Neural Network for change detection. We introduce conditionalized convolution (CondConv) and ConvNeXt block into the change detection task to improve the performance of the network in recognizing complex objects, and we can refer to this method as DNNet for short. In this paper, the bi-temporal images are first subjected to differential analysis, and then the convolutional neural network is trained with pseudo-labels generated by clustering. The effectiveness of DNNet is verified on three real SAR datasets and compared with several state-of-the-art methods. Experiments results show that the proposed method has better effectiveness.

Citation Download Citation

Qianqian Liu, Xiaorong Xue, Kejun Liu, and Haiyan Liu "An effective unsupervised method for change detection in SAR images", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 1270508 (27 June 2023); https://doi.org/10.1117/12.2680149

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KEYWORDS

Convolution

Synthetic aperture radar

Remote sensing

Convolutional neural networks

Feature extraction

Ablation

Sensors

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