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A Postdetection Framework With Optimal Transport for Multiclass Object Change Detection
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AI
DOI：10.1109/LGRS.2025.3541828.png)
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Abstract
En 中文
Current research on change detection has made significant progress on large-scale landscapes and buildings. However, there is a lack of exploration into the status changes of multiclass and time-sensitive objects across different temporal of remote sensing images (RSIs). To bridge this gap, we first introduce a task termed multiclass object change detection (MCOCD) and then construct a dedicated dataset dubbed aircraft change detection (ACD). Furthermore, we propose a postdetection framework to address this task. In the framework, we first feed bitemporal RSIs into an object detector to obtain the bounding boxes (BBOXs) of predefined classes. Subsequently, we utilize the intersection over union (IoU)-based distance to ascertain changes. Nervelessly, due to the dense arrangement of objects in RSIs, directly using IoU-based distance to determine changes results in one-to-many or many-to-one matching problems. To address this issue, we propose an optimal transport (OT) module to compute the global optimal matching of distance matrices, which are bidirectional augmented with dustbin nodes. Finally, the detected objects that are matched with the dustbin nodes being regarded as the changed ones. Extensive experiments demonstrate the effectiveness of our methods.
Keywords:
Change detection
multiclass object change detection (MCOCD)
optimal transport (OT)
optimal transport (OT)
remote sensing
remote sensing
remote sensing
Journal
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16.4
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1.0W
Citations:
5.1K