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...
By Anuvab Sen, Maneet Chatterjee, Aparup Ghosh, Udayon Sen, Arnav Aditya, Yixin Zhang
arXiv:2605. 15375v2 Announce Type: replace-cross Abstract: Remote sensing change detection (RSCD) localises changes between two images of the same geographic region.
By Bla\v{z} Rolih, Matic Fu\v{c}ka, Filip Wolf, Luka \v{C}ehovin Zajc
Remote sensing change detection for real-world monitoring often relies on imperfect heterogeneous observations, where pre- and post-event images may be asynchronous, cross-sensor, or affected by illumination, seasonal, and modality shifts. This setting is especially challenging for EO-SAR disaster mapping, where nuisance variation can resemble structural damage.
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.
MambaMPD is a new segmentation framework that leverages Vision Mamba models for marine pollution detection in remote‑sensing imagery. It introduces two structural priors—Frequency‑Aware Augmentation (FAA) and multi‑scale Edge‑Guided Attention (EGA)—to better capture low‑contrast, fragmented pollution patterns and sharpen boundaries. Experiments on the MADOS and M4D datasets show that MambaMPD outperforms existing methods in mIoU while using far less computation than foundation‑model approaches.
By Shuaiyu Chen, Wei Han, Peng Ren, Chunbo Luo, Zeyu Fu
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.
By Bla\v{z} Rolih, Matic Fu\v{c}ka, Filip Wolf, Luka \v{C}ehovin Zajc