Change State Space Models for Remote Sensing Change Detection
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.
67ff97c20816a39af5ec7c96
Elman Ghazaei
extracted_confirmed
2025-07-09T08:23:08.225Z
67ff950b9e4824de182acf83
/avatars/d8bae10afdd7457a08ec9554f79c7429.svg
Elman Ghazaei
user
ElmanGhazaei
67ff97c20816a39af5ec7c97
Erchan Aptoula
Change State Space Models for Remote Sensing Change Detection
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.
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.
ConvNets Vision Transformers State Space Models Change Detection bi-temporal images computational efficiency benchmark datasets