Change detection of high-resolution remote sensing images based on Siamese network

doi:10.6046/zrzyyg.2021122

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Abstract

With the improvement of the spatial resolution of remote sensing images, the imaging features of ground objects have become increasingly complex. As a result, the change detection methods of remote sensing images based on texture expression and local semantics are difficult to meet the demand. To improve the change detection accuracy of high-resolution remote sensing images, this study constructed a large-scale remote sensing-based human activity change detection dataset (HRHCD-1.0) with a high resolution of 0.8~2 m. Moreover, this study designed an attention-based Siamese change detection network with a strong capability to extract contextual semantic features by introducing spatial attention and channel attention mechanisms. In the model comparative experiment, the attention-based Siamese change detection network proposed in this study increased the mean intersection over union on the validation set by 24% and showed more complete detection results compared to the models using non-attention mechanisms, effectively alleviating the problems of poor boundary, local omission, and holes of models using non-attention mechanisms. The post-processing method allows for small polygon removal, hole filling, and graphic smoothing of the detection results, improving the processing graphic effects of polygons. Furthermore, the increase in the sample size in the training of change detection significantly improves the application accuracy and generalization ability of the attention-based Siamese change detection network proposed in this study.

Keywords high-resolution remote sensing images change detection deep learning attention mechanism

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TP79

Issue Date: 14 March 2022

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	Bai XUE
	Yizhe WANG
	Shuhan LIU
	Mingyu YUE
	Yiying WANG
	Shihu ZHAO

Cite this article:

Bai XUE,Yizhe WANG,Shuhan LIU, et al. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021122 OR https://www.gtzyyg.com/EN/Y2022/V34/I1/61

Tab.1 Main open source high-resolution remote sensing change detection data set

Fig.1 Examples of positive and negative examples of HRHCD data set

Fig.2 Siamese attention mechanism change detection network structure

Tab.2 Comparison of detection results of different networks

Fig.3 Results with post-processing optimization

Tab.3 Model application effects under different training data scales

Fig.4 Examples of misdetected patterns for non-business types

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