Journal
REMOTE SENSING
Volume 12, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/rs12030389
Keywords
remote sensing; arbitrary-oriented object detection; feature pyramid network; attention mechanism; mask
Categories
Funding
- National Natural Science Foundation of China [61772399, U1701267, 61773304, 61672405, 61772400]
- Key Research and Development Program in Shaanxi Province of China [2019ZDLGY09-05]
- Program for Cheung Kong Scholars and Innovative Research Team in University [IRT_15R53]
- Technology Foundation for Selected Overseas Chinese Scholar in Shaanxi [2017021, 2018021]
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Object detection has made significant progress in many real-world scenes. Despite this remarkable progress, the common use case of detection in remote sensing images remains challenging even for leading object detectors, due to the complex background, objects with arbitrary orientation, and large difference in scale of objects. In this paper, we propose a novel rotation detector for remote sensing images, mainly inspired by Mask R-CNN, namely RADet. RADet can obtain the rotation bounding box of objects with shape mask predicted by the mask branch, which is a novel, simple and effective way to get the rotation bounding box of objects. Specifically, a refine feature pyramid network is devised with an improved building block constructing top-down feature maps, to solve the problem of large difference in scales. Meanwhile, the position attention network and the channel attention network are jointly explored by modeling the spatial position dependence between global pixels and highlighting the object feature, for detecting small object surrounded by complex background. Extensive experiments on two remote sensing public datasets, DOTA and NWPUVHR -10, show our method to outperform existing leading object detectors in remote sensing field.
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