Abstract
Detecting object in remote sensing images remains a challenge due to multi-scale objects, complex ground environment and large image size despite of the fast development of machine learning and computer vision technology in recent years. The primary difficulty lies in the fast and accurate location of candidate bounding boxes from a large-size remote sensing image. In this letter, we propose a novel remote sensing object detection method inspired by the recent-popular technique, Object Proposals, to quickly generate high-quality object bounding box locations in remote sensing images. A simple but effective objectness measurement, based on the image gradients and its variants, is proposed. Moreover, to evaluate the effectiveness of our method, we complete the subsequent detection flow based on the convolution neural networks as a standard detection baseline. Experiments show that our method is able to produce high-quality proposals with a desirable computational speed.
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Acknowledgement
The work was supported by the funding project of the State Key Laboratory of Space-Ground Integrated Information Technology (SKL-SGIIT), under the Grant 2016-SGIIT-KFJJ-YG-03. The authors also would like to thank Piotr Dollar for his image processing toolbox.
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Ding, H., Luo, Q., Zou, Z., Guo, C., Shi, Z. (2017). Object Detection with Proposals in High-Resolution Optical Remote Sensing Images. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_27
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DOI: https://doi.org/10.1007/978-3-319-68935-7_27
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