A Remote Sensing Image Change Detection Method Integrating Layer Exchange and Channel-Spatial Differences

• URL: http://arxiv.org/abs/2501.10905v1
• Date: Sun, 19 Jan 2025 00:14:20 GMT
• Title: A Remote Sensing Image Change Detection Method Integrating Layer Exchange and Channel-Spatial Differences
• Authors: Sijun Dong, Fangcheng Zuo, Geng Chen, Siming Fu, Xiaoliang Meng,
• Abstract summary: Change detection in remote sensing imagery is a critical technique for Earth observation. In deep learning, the spatial and channel dimensions of feature maps represent different information from the original images. In this study, we found that difference information can be computed not only from the spatial dimension of bi-temporal features but also from the channel dimension.
• Score: 4.370130821531168
• License:
• Abstract: Change detection in remote sensing imagery is a critical technique for Earth observation, primarily focusing on pixel-level segmentation of change regions between bi-temporal images. The essence of pixel-level change detection lies in determining whether corresponding pixels in bi-temporal images have changed. In deep learning, the spatial and channel dimensions of feature maps represent different information from the original images. In this study, we found that in change detection tasks, difference information can be computed not only from the spatial dimension of bi-temporal features but also from the channel dimension. Therefore, we designed the Channel-Spatial Difference Weighting (CSDW) module as an aggregation-distribution mechanism for bi-temporal features in change detection. This module enhances the sensitivity of the change detection model to difference features. Additionally, bi-temporal images share the same geographic location and exhibit strong inter-image correlations. To construct the correlation between bi-temporal images, we designed a decoding structure based on the Layer-Exchange (LE) method to enhance the interaction of bi-temporal features. Comprehensive experiments on the CLCD, PX-CLCD, LEVIR-CD, and S2Looking datasets demonstrate that the proposed LENet model significantly improves change detection performance. The code and pre-trained models will be available at: https://github.com/dyzy41/lenet.

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