title: Change State Space Models for Remote Sensing Change Detection
summary: Despite their frequent use for change detection, both ConvNets and Vision
  transformers (ViT) exhibit well-known limitations, namely the former struggle
  to model long-range dependencies while the latter are computationally
  inefficient, rendering them challenging to train on large-scale datasets.
  Vision Mamba, an architecture based on State Space Models has emerged as an
  alternative addressing the aforementioned deficiencies and has been already
  applied to remote sensing change detection, though mostly as a feature
  extracting backbone. In this article the Change State Space Model is
  introduced, that has been specifically designed for change detection by
  focusing on the relevant changes between bi-temporal images, effectively
  filtering out irrelevant information. By concentrating solely on the changed
  features, the number of network parameters is reduced, enhancing significantly
  computational efficiency while maintaining high detection performance and
  robustness against input degradation. The proposed model has been evaluated via
  three benchmark datasets, where it outperformed ConvNets, ViTs, and Mamba-based
  counterparts at a fraction of their computational complexity. The
  implementation will be made available at https://github.com/Elman295/CSSM upon
  acceptance.
paper_authors._id: 67ff97c20816a39af5ec7c96
paper_authors.name: Elman Ghazaei
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-04-16T11:43:00.595Z
paper_authors._id: 67ff950b9e4824de182acf83
paper_authors.avatarUrl: /avatars/d8bae10afdd7457a08ec9554f79c7429.svg
paper_authors.fullname: Elman Ghazaei
paper_authors.type: user
paper_authors.user: ElmanGhazaei
paper_authors._id: 67ff97c20816a39af5ec7c97
paper_authors.name: Erchan Aptoula
paper_title: Change State Space Models for Remote Sensing Change Detection
paper_summary: Despite their frequent use for change detection, both ConvNets and Vision
  transformers (ViT) exhibit well-known limitations, namely the former struggle
  to model long-range dependencies while the latter are computationally
  inefficient, rendering them challenging to train on large-scale datasets.
  Vision Mamba, an architecture based on State Space Models has emerged as an
  alternative addressing the aforementioned deficiencies and has been already
  applied to remote sensing change detection, though mostly as a feature
  extracting backbone. In this article the Change State Space Model is
  introduced, that has been specifically designed for change detection by
  focusing on the relevant changes between bi-temporal images, effectively
  filtering out irrelevant information. By concentrating solely on the changed
  features, the number of network parameters is reduced, enhancing significantly
  computational efficiency while maintaining high detection performance and
  robustness against input degradation. The proposed model has been evaluated via
  three benchmark datasets, where it outperformed ConvNets, ViTs, and Mamba-based
  counterparts at a fraction of their computational complexity. The
  implementation will be made available at https://github.com/Elman295/CSSM upon
  acceptance.
paper_ai_summary: A Change State Space Model is introduced for efficient change detection, improving on ConvNets and Vision Transformers by focusing on relevant changes, reducing parameters, and maintaining high performance.
paper_ai_keywords: ConvNets Vision Transformers State Space Models Change Detection bi-temporal images computational efficiency benchmark datasets