arXiv Computer Vision
ChangeJEPA: Predict Before You Compare for Remote Sensing Change Detection
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arXiv AI
Jul 28
ChangeFlow -- Latent Rectified Flow for Change Detection in Remote Sensing
arXiv:2605. 15375v2 Announce Type: replace-cross Abstract: Remote sensing change detection (RSCD) localises changes between two images of the same geographic region.
arXiv Computer Vision
Sep 24
From Change Captions to Change Detection: Semantic-Appearance Agreement Framework for Remote Sensing Change Detection
The paper proposes a weakly supervised remote sensing change detection method that uses change captions as the sole supervision signal, eliminating the need for pixel‑level change masks. It introduces a caption‑driven generation pipeline to create bi‑temporal image pairs with controlled changes and a Semantic‑Appearance Agreement Framework (SAAF) that fuses caption‑grounded semantic responses with RGB differences for accurate change localization. Experiments on the Flair‑RSGen and WHU‑CDC datasets demonstrate that SAAF outperforms existing limited‑supervision baselines in macro‑averaged IoU and F1 metrics.
arXiv AI
Sep 2
Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbations
The paper introduces MaSoN, an end-to-end unsupervised remote sensing change detection framework that synthesises diverse changes directly in latent feature space during training. By generating changes based on feature statistics of the target data, MaSoN produces data‑driven variations that align with the target domain and can be applied to new modalities such as SAR and multispectral imagery. The method achieves a 14.1 percentage point improvement in average F1 score across five benchmarks, demonstrating strong generalisation across diverse change types.
Hugging Face Trending Papers
Jul 8
ASFR-Net: Adversarial Alignment and Spatio-Frequency Refinement Network for Heterogeneous Remote Sensing Image Change Detection
The core challenge of heterogeneous change detection in remote sensing imagery lies in effectively decoupling genuine land-cover changes from significant modal disparities caused by distinct imaging mechanisms. These intrinsic inconsistencies are prone to introducing pseudo-changes, thereby constraining detection accuracy.
arXiv Computer Vision
Sep 24
Temporally Ordered Region-Token Mamba with Logit-Space Diffusion for Remote Sensing Change Detection
arXiv:2609.27149v1 Announce Type: new Abstract: Remote sensing change detection requires both global reasoning across bitemporal images and precise localization of changed regions. However, dense att...
arXiv Computer Vision
Oct 26
Next-Frequency Autoregressive Modeling for Remote Sensing Change Detection
arXiv:2607.25815v2 Announce Type: replace Abstract: Remote sensing change detection identifies land-cover changes from bi-temporal images. Most existing methods follow a one-shot dense prediction par...