基于深度强化学习的遥感图像可解释目标检测方法

随着遥感技术的飞速发展,遥感图像目标检测在资源勘探、城市规划、自然灾害评估等方面得到广泛应用.遥感影像背景复杂、目标尺度较小,难以检测.针对此问题,文中提出基于深度强化学习的遥感图像可解释目标检测方法.首先,将深度强化学习应用于超快速区域神经网络中的候选区域生成网络,修改激励函数,提高对遥感图像的检测精度.然后,将原有参数量较大的主干网络轻量化,提高方法的检测速度和可移植性.最后,利用网络解剖方法对隐层表征的可解释性进行量化,赋予方法人类理解的可解释性概念.实验表明,文中方法在3个公开的遥感数据集上的性能有所提升.通过改进的网络解剖方法进一步验证方法的有效性.

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

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.