Multimodal remote sensing change detection: An image matching perspective

2026/02/06 by Hongruixuan Chen, Cuiling Lan, Jian Song +4
Engineering · Environmental Science · #Automated Road and Building Extraction #Remote Sensing in Agriculture #Remote-Sensing Image Classification

paper · doi:10.1016/j.isprsjprs.2026.02.004

openalex publication_date 2026/02/06 · openalex created_date 2026/02/07 · openalex updated_date 2026/07/30

Abstract

Change Detection (CD) between images with different modalities is a fundamental capability for remote sensing. In this work, we pinpoint the commonalities between Multimodal Change Detection (MCD) and Multimodal Image Matching (MIM). Accordingly, we present a new unsupervised CD framework designed from the perspective of Image Matching (IM), called IM4CD. It unifies the IM and CD tasks into a single, coherent framework. In this framework, we abandon the prevalent strategy in MCD to compare per-pixel image features, since it is in practice quite difficult to design features that are truly invariant across modalities. Instead, we propose to compute similarity by local template matching and utilize the spatial offset of response peaks to represent change intensity between images with different modalities, and then to integrate it tightly with the co-registration of the two images, which anyway includes such a matching step. In this way, the same off-the-shelf descriptors used for MIM also support MCD. In other words, we first extract modality-independent features, then detect salient points to obtain initial pairs of corresponding Control Points (CP). When matching those points to accurately register the images, CP pairs located in unchanged areas show low residuals, whereas those in changed areas show high residuals. The CPs can then be connected into a Conditional Random Field (CRF), leveraging modality-independent structural relationships to estimate dense change maps. Experimental results show the effectiveness of our method, including robustness to registration errors, its compatibility with different image descriptors, and promising potential for challenging real-world disaster response scenarios.

Citations

• Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimation
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