arXiv:2606. 10328v1 Announce Type: cross Abstract: The integration of spatial and spectral information is beneficial to the improvement of change detection performance.
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The integration of spatial and spectral information is beneficial to the improvement of change detection performance. However, existing methods cannot efficiently suppress the influences of spatial and spectral differences in unchanged areas.
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.
The paper introduces SGFNet, a Semantic‑Guided Fusion Network for classifying multi‑source remote sensing images. It features a Semantic Mixing Convolution Block that generates semantic‑aware kernels based on contextual relationships, and a Frequency Modulated Fusion Block that fuses cross‑modal information in the frequency domain to mitigate spatial misalignment. Experiments on the Augsburg and Houston 2018 datasets show SGFNet consistently outperforms state‑of‑the‑art methods.
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:2606. 10329v1 Announce Type: cross Abstract: As one of the most destructive natural disasters, earthquakes have struck many countries around the world in recent years, causing serious economic losses.
arXiv:2606. 27410v1 Announce Type: cross Abstract: The primary goal of Remote Sensing Image Change Captioning (RSICC) is to automatically generate descriptions of changes between remote sensing images captured at different time points.