Abstract
In this paper, we concentrate on tackling the problems of object detection in very-high-resolution (VHR) remote sensing images. The main challenges of object detection in VHR remote sensing images are: (1) VHR images are usually too large and it will consume too much time when locating objects; (2) high false alarm because background dominate and is complex in VHR images. To address the above challenges, a new method is proposed to build two-stage object detection model. Our proposed method can be divided into two processes: (1) we use twice pruning to get region proposal convolutional neural network which is used to predict region proposals; (2) and we use once pruning to get classification convolutional neural network which is used to analyze the result of the first stage and output the class labels of proposals. The experimental results show that the proposed method has high precision and is significantly faster than the state-of-the-art methods on NWPU VHR-10 remote sensing dataset.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (61472161), Science & Technology Development Project of Jilin Province (20180101334JC), Natural Science Foundation of Hunan Province (No. 2018JJ3479).
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Wang, S., Wang, M., Zhao, X., Liu, D. (2018). Two-Stage Object Detection Based on Deep Pruning for Remote Sensing Image. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_12
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