Journal
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)
Volume -, Issue -, Pages 1454-1457Publisher
IEEE
DOI: 10.1109/igarss.2019.8898573
Keywords
Object detection; instance segmentation; Mask R-CNN; remote sensing images; precise Rot Pooling
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Funding
- National Natural Science Foundation of China [61501098]
- High Resolution Earth Observation Youth Foundation [GFZX04061502]
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Object detection in very high-resolution (VHR) remote sensing images is a fundamental and challenging problem due to the complex environments. In this paper, a precise mask region convolutional neural network (precise Mask R-CNN) is presented for object detection and instance segmentation in VHR remote sensing images. This method generates bounding boxes and segmentation masks for each instance of an object in the image. Contrary to regions of interest (RoI) Align whose sample points is pre-defined and not adaptive the size of the bin, the proposed precise Rot pooling can directly compute the two-order integral based on the continuous feature map to avoid loss of precision. The experiments on NWPU VHR-10 dataset show that the presented precise Mask R-CNN improves the accuracy of object detection and instance segmentation for VHR remote sensing images. Furthermore, it promotes the application of instance segmentation in VHR remote sensing.
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