一种基于CNN的SAR图像变化检测方法  

A Novel Approach to Change Detection in SAR Images with CNN Classification

作　　者：徐真[1,2] 王宇 李宁[1] 张衡[1,2] 张磊 Xu Zhen;Wang Robert;Li Ning;Zhang Heng;Zhang Lei(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;University of Chinese Academy of Sciences, Beijing 100049, China)

机构地区：[1]中国科学院电子学研究所,北京100190 [2]中国科学院大学,北京100049

出　　处：《雷达学报（中英文）》2017年第5期483-491,共9页Journal of Radars

基　　金：国家重点研发计划(2017YFB0502700);中科院国防科技创新基金面上项目~~

摘　　要：该文提出了一种基于卷积神经网络(CNN)及有效图像预处理的合成孔径雷达(SAR)图像变化检测方法。为了验证方法的有效性,以2011年日本仙台地区地震导致的城区变化为例进行了研究。在预处理中分别利用DEM模型以及Otsu方法对SAR图像中的山体和水体进行了提取和去除。利用多层卷积神经网络从SAR图像中自动学习目标特征,再利用学习到的特征对图像进行分类。训练集和测试集的分类精度分别达到了98.25%和97.86%。利用图像差值法得到分类后的SAR图像变化检测结果,并验证了该方法的准确性和有效性。另外,文中给出了基于CNN的变化检测方法和传统方法的对比结果。结果表明,相对于传统方法,基于CNN的变化检测方法具有更高的检测精度。This paper presents a novel Synthetic Aperture Radar（SAR）-image-change-detection method, which integrates effective-image preprocessing and Convolutional Neural Network（CNN） classification. To validate the efficiency of the proposed method, two SAR images of the same devastated region obtained by Terra SAR-X before and after the 2011 Tohoku earthquake are investigated. During image preprocessing, the image backgrounds such as mountains and water bodies are extracted and removed using Digital Elevation Model（DEM） model and Otsu＇s thresholding method. A CNN is employed to automatically extract hierarchical feature representation from the data. The SAR image is then classified with the theoretically obtained features.The classification accuracies of the training and testing datasets are 98.25% and 97.86%, respectively. The changed areas between two SAR images are detected using image difference method. The accuracy and efficiency of the proposed method are validated. In addition, with other traditional methods as comparison, this paper presents change-detection results using the proposed method. Results show that the proposed method has higher accuracy in comparison with traditional change-detection methods.

关 键 词：SAR图像 变化检测 卷积神经网络 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]