A Bridge Neural Network-Based Optical-SAR Image Joint Intelligent Interpretation Framework  

作　　者：Meiyu Huang Yao Xu Lixin Qian Weili Shi Yaqin Zhang Wei Bao Nan Wang Xuejiao Liu Xueshuang Xiang 

机构地区：[1]Qian Xuesen Laboratory of Space Technology,China Academy of Space Technology,Beijing,China

出　　处：《Space(Science & Technology)》2021年第1期97-106,共10页空间科学与技术（英文）

基　　金：This is supported by the Beijing Nova Program of Science and Technology under Grant Z191100001119129;the National Natural Science Foundation of China 61702520.

摘　　要：The current interpretation technology of remote sensing images is mainly focused on single-modal data,which cannot fully utilize the complementary and correlated information of multimodal data with heterogeneous characteristics,especially for synthetic aperture radar(SAR)data and optical imagery.To solve this problem,we propose a bridge neural network-(BNN-)based optical-SAR image joint intelligent interpretation framework,optimizing the feature correlation between optical and SAR images through optical-SAR matching tasks.It adopts BNN to effectively improve the capability of common feature extraction of optical and SAR images and thus improving the accuracy and application scenarios of specific intelligent interpretation tasks for optical-SAR/SAR/optical images.Specifically,BNN projects optical and SAR images into a common feature space and mines their correlation through pair matching.Further,to deeply exploit the correlation between optical and SAR images and ensure the great representation learning ability of BNN,we build the QXS-SAROPT dataset containing 20,000 pairs of perfectly aligned optical-SAR image patches with diverse scenes of high resolutions.Experimental results on optical-to-SAR crossmodal object detection demonstrate the effectiveness and superiority of our framework.In particular,based on the QXSSAROPT dataset,our framework can achieve up to 96%high accuracy on four benchmark SAR ship detection datasets.

关 键 词：MODAL utilize MATCHING 

分 类 号：TN9[电子电信—信息与通信工程]