Interpretable Object Detection Method for Remote Sensing Image Based on Deep Reinforcement Learning

被引：0
|
作者：
Zhao J. [1 ,2 ,3 ]
Zhang D. [1 ,2 ]
Zhou Y. [1 ,2 ]
Chen S. [1 ,2 ]
Tang J. [1 ,2 ]
Yao R. [1 ,2 ]
机构：
[1] School of Computer Science and Technology, China University of Mining and Technology, Xuzhou
[2] Engineering Research Center of Mine Digitization of Ministry of Education, China University of Mining and Technology, Xuzhou
[3] Innovation Research Center of Disaster Intelligent Prevention and Emergency Rescue, China University of Mining and Technology, Xuzhou
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Deep Reinforcement Learning; Object Detection; Remote Sensing Image; Reward Function;
D O I：
10.16451/j.cnki.issn1003-6059.202109001
中图分类号：
学科分类号：
摘要：
With the rapid development of remote sensing technology, object detection for remote sensing image is widely applied in many fields,such as resource exploration, urban planning and natural disaster assessment. Aiming at the complex background and the small target scale of remote sensing images, an interpretable object detection method for remote sensing image based on deep reinforcement learning is proposed. Firstly, deep reinforcement learning is applied to the region proposal network in faster region-convolutional neural network to improve the detection accuracy of remote sensing images by modifying the excitation function. Secondly, the detection speed and portability of the model are improved by lightening the original backbone network with a large number of parameters. Finally, the interpretability of the hidden layer representation in the model is quantified using the network anatomy method to endow the model with an interpretable concept of human understanding. Experiments on three public remote sensing datasets show that the performance of the proposed method is improved and the effectiveness of the proposed method is verified by the improved network anatomy method. © 2021, Science Press. All right reserved.
引用
收藏
页码：777 / 786
页数：9
相关论文
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