LaVIDE: A Language-Vision Discriminator for Detecting Changes in Satellite Image with Map References

• URL: http://arxiv.org/abs/2411.19758v1
• Date: Fri, 29 Nov 2024 15:04:40 GMT
• Title: LaVIDE: A Language-Vision Discriminator for Detecting Changes in Satellite Image with Map References
• Authors: Shuguo Jiang, Fang Xu, Sen Jia, Gui-Song Xia,
• Abstract summary: Change detection is significantly hindered when only a single image is available. We propose a textbfLanguage-textbfVIsion textbfDiscriminator for dtextbfEtecting changes in satellite image with map references, namely ours.
• Score: 31.376693854087122
• License:
• Abstract: Change detection, which typically relies on the comparison of bi-temporal images, is significantly hindered when only a single image is available. Comparing a single image with an existing map, such as OpenStreetMap, which is continuously updated through crowd-sourcing, offers a viable solution to this challenge. Unlike images that carry low-level visual details of ground objects, maps convey high-level categorical information. This discrepancy in abstraction levels complicates the alignment and comparison of the two data types. In this paper, we propose a \textbf{La}nguage-\textbf{VI}sion \textbf{D}iscriminator for d\textbf{E}tecting changes in satellite image with map references, namely \ours{}, which leverages language to bridge the information gap between maps and images. Specifically, \ours{} formulates change detection as the problem of ``{\textit Does the pixel belong to [class]?}'', aligning maps and images within the feature space of the language-vision model to associate high-level map categories with low-level image details. Moreover, we build a mixture-of-experts discriminative module, which compares linguistic features from maps with visual features from images across various semantic perspectives, achieving comprehensive semantic comparison for change detection. Extensive evaluation on four benchmark datasets demonstrates that \ours{} can effectively detect changes in satellite image with map references, outperforming state-of-the-art change detection algorithms, e.g., with gains of about $13.8$\% on the DynamicEarthNet dataset and $4.3$\% on the SECOND dataset.

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