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

arXiv 2501.10905 ↗First posted 19 Jan 2025cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 63 references that are in the map. First posted on arXiv 19 Jan 2025.

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19 of 63 references.

References · 19

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  7. 2021Remote Sensing Image Change Detection with Transformerscited by 306
  8. 2023SpectralGPT: Spectral Remote Sensing Foundation Modelcited by 252
  9. 2022A Transformer-Based Siamese Network for Change Detectioncited by 244
  10. 2022Changer: Feature Interaction is What You Need for Change Detectioncited by 79
  11. 2023Adapting Segment Anything Model for Change Detection in HR Remote Sensing Imagescited by 56
  12. 2024CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detectioncited by 48
7 more
  1. 2023A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detectioncited by 47
  2. 2024Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagerycited by 30
  3. 2023Remote Sensing Change Detection With Transformers Trained from Scratchcited by 22
  4. 2023Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localizationcited by 21
  5. 2024RSBuilding: Towards General Remote Sensing Image Building Extraction and Change Detection with Foundation Modelcited by 12
  6. 2024PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing Imagerycited by 12
  7. 2024EfficientCD: A New Strategy For Change Detection Based With Bi-temporal Layers Exchangedcited by 4

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