mlsanityData to 14 Oct 2026

A Spatial Semantics and Continuity Perception Attention for Remote Sensing Water Body Change Detection

arXiv 2511.16143 ↗First posted 20 Nov 2025cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Remote sensing Water Body Change Detection (WBCD) aims to detect water body surface changes from bi-temporal images of the same geographic area. Recently, the scarcity of high spatial resolution datasets for WBCD restricts its application in urban and rural regions, which require more accurate positioning. Meanwhile, previous deep learning-based methods fail to comprehensively exploit the spatial semantic and structural information in deep features in the change detection networks. To resolve these concerns, we first propose a new dataset, HSRW-CD, with a spatial resolution higher than 3 meters for WBCD. Specifically, it contains a large number of image pairs, widely covering various water body types. Besides, a Spatial Semantics and Continuity Perception (SSCP) attention module is designed to fully leverage both the spatial semantics and structure of deep features in the WBCD networks, significantly improving the discrimination capability for water body. The proposed SSCP has three components: the Multi-Semantic spatial Attention (MSA), the Structural Relation-aware Global Attention (SRGA), and the Channel-wise Self-Attention (CSA). The MSA enhances the spatial semantics of water body features and provides precise spatial semantic priors for the CSA. Then, the SRGA further extracts spatial structure to learn the spatial continuity of the water body. Finally, the CSA utilizes the spatial semantic and structural priors from the MSA and SRGA to compute the similarity across channels. Specifically designed as a plug-and-play module for water body deep features, the proposed SSCP allows integration into existing WBCD models. Numerous experiments conducted on the proposed HSRW-CD and Water-CD datasets validate the effectiveness and generalization of the SSCP. The code of this work and the HSRW-CD dataset will be accessed at https://github.com/QingMa1/SSCP.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 33 of its 67 references that are in the map. First posted on arXiv 20 Nov 2025.

Builds on

33 of 67 references.

References · 33

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  5. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  6. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  7. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  8. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  9. 2017Squeeze-and-Excitation Networkscited by 4,789
  10. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  11. 2017Non-local Neural Networkscited by 2,293
  12. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
21 more
  1. 2018Group Normalizationcited by 1,524
  2. 2024VMamba: Visual State Space Modelcited by 969
  3. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
  4. 2022MaxViT: Multi-Axis Vision Transformercited by 476
  5. 2019Selective Kernel Networkscited by 403
  6. 2019Dynamic Convolution: Attention over Convolution Kernelscited by 356
  7. 2022A Transformer-Based Siamese Network for Change Detectioncited by 244
  8. 2021Coordinate Attention for Efficient Mobile Network Designcited by 241
  9. 2019Relation-Aware Global Attention for Person Re-identificationcited by 124
  10. 2022Changer: Feature Interaction is What You Need for Change Detectioncited by 79
  11. 2024Poly Kernel Inception Network for Remote Sensing Detectioncited by 52
  12. 2023A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detectioncited by 47
  13. 2024Spatial-Mamba: Effective Visual State Space Models via Structure-aware State Fusioncited by 41
  14. 2019Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networkscited by 33
  15. 2024HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Imagescited by 30
  16. 2024Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagerycited by 30
  17. 2024CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applicationscited by 12
  18. 2024SCSA: Exploring the Synergistic Effects Between Spatial and Channel Attentioncited by 10
  19. 2024DDLNet: Boosting Remote Sensing Change Detection with Dual-Domain Learningcited by 6
  20. 2024Rethinking Remote Sensing Change Detection With A Mask Viewcited by 5
  21. 2024CDXLSTM: Boosting Remote Sensing Change Detection with Extended Long Short-Term Memorycited by 4

Built on by

Not cited yet.