仿真图像作为模板的遥感影像小目标检测方法  

Remote Sensing Image Small Target Detection Method Using Simulation Image as Template

作　　者：曹亚明 肖奇 杨震 CAO Yaming;XIAO Qi;YANG Zhen(National Space Science Center,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院国家空间科学中心,北京100190 [2]中国科学院大学,北京100049

出　　处：《计算机工程与应用》2022年第17期111-119,共9页Computer Engineering and Applications

基　　金：中国科学院基础前沿科学研究计划(E0223301)。

摘　　要：随着传感器技术和航空遥感技术的不断进步,遥感影像的质量和数量也得到了极大的提高,而遥感影像中的目标检测是理解和分析遥感影像所面临的一个基本问题。针对神经网络在遥感影像小目标检测任务中难以提取足够多的有效特征、遥感小目标易受云雾遮挡等问题,提出了一种基于仿真图像模板匹配的方法,通过特征融合的方式成功地将该方法应用于遥感影像小目标检测任务。成像仿真技术生成的仿真图像包含了更多的遥感小目标特征,如几何形状、材质等。在与深度学习结合之后,更多的特征可以提升神经网络检测遥感影像小目标的准确率。实验结果表明将基于仿真图像的模板匹配方法应用于深度学习之后,对于遥感影像小目标检测取得了较好的效果,尤其是针对受到云雾等天气干扰的小目标。With the continuous progress of sensor technology and aerial remote sensing technology, the quality and quantity of remote sensing images have been greatly improved. Target detection in remote sensing images is a basic problem in understanding and analyzing remote sensing images. Aiming at the problems that neural network is difficult to extract enough effective features in remote sensing image small target detection task, and remote sensing small target is easily blocked by cloud and fog, a method based on simulation image template matching is proposed, which is successfully applied to remote sensing image small target detection task by feature fusion. The simulation image generated by imaging simulation technology contains more features of remote sensing small target, such as geometry, material and so on. In the process of combining with deep learning, more features can improve the accuracy of neural network for small target detection in remote sensing image. The results show that the template matching method based on the simulation image is applied to the deep learning, and it achieves good detection results for small targets in remote sensing images, especially for small targets disturbed by the weather such as clouds and fog.

关 键 词：遥感影像 目标检测 仿真图像 模板 深度学习 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]