Abstract
Natural disasters frequently have caused huge impact on life and property losses, in Southwest China. To provide assistance for disaster relief, areas damaged in natural disasters is quickly located by utilizing satellite remote sensing images based-deep learning object detection technology. However, the current detection technology, for the detection of damaged objects discretely in the disaster area, has some challenges, such as partial missing of multi-source images and extremely sparse targets with weak features or occlusion at large scales. Furthermore, we propose an object detection network based on dynamic extraction of multi-source images features to solve above problems. To train our proposed network, we collect multi-source remote sensing images before and after the disaster. Finally, it is verified that when the detection error rate is less than 5%, the accuracy of the detection model reaches more than 85%.
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