高分辨率单极化SAR图像慢动船只散射特性稳健高层表征研究

项目名称： 高分辨率单极化SAR图像慢动船只散射特性稳健高层表征研究

项目编号： No.41501356

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 田巳睿

作者单位： 南京理工大学

项目金额： 20万元

中文摘要： 高分辨率SAR图像慢动船只分类可为领海安全、海上执法和海洋环境保护等活动提供全天时、全天候、大范围和高精度的船只信息，是海洋遥感的研究热点之一。针对其面临的特征高易变、米级以上全极化数据不足和有标记样本缺乏等问题，本项目拟开展基于深度学习的高分辨率单极化SAR图像慢动船只散射特性高层表征方法研究。建立慢动船只SAR成像模型，分析慢动船只散射特性反演失真成因，设计船只运动多普勒相位补偿和图像重聚焦方法，提高散射特性反演精度。采用时频超图像模型，在单极化重聚焦SAR图像中反演船只低层散射特性，构建多维散射特征图像。建立基于卷积-池化结构的栈式降噪卷积稀疏自编码器模型，将深度学习自编码器扩展到多维特征图像域，提取慢动船只散射特性潜在高层表征，改善有限样本下船只分类性能。本项目提出了一种SAR图像船只稳健物理特征提取的新方法，对推动SAR船只分类技术在国防、交通、环境等领域的应用具有重要意义。

中文关键词： 特征提取；目标识别；深度学习；非监督特征学习；主动微波遥感

英文摘要： Slowly moving ship classification using synthetic aperture radar image (SAR ship classification) plays a crucial role in oceanographical remote sensing, providing precise ship information for territorial security, maritime law enforcement and environment protection in all day and night, under various weather conditions, and over a relatively large area. SAR ship classification confronts with many obstacles, including the unstable low level image features, lack of quad-pol SAR images with resolution better than 1m, and insufficient labeled ship samples. To solve these problems, a deep learning based robust high level representation of scattering characteristics for slowly moving ship in single polarimetric high resolution SAR image is proposed. In this study, the main research efforts include: 1) establishing the SAR imaging model for slowly moving ship to analyze the cause of scattering characteristics inversion errors and devise the Doppler compensation method based on the refocusing process; 2) inverting the scattering characteristics of ships in the refocused single polarimetric SAR image using the time-frequency hyperimage model and constructing the multi-dimension scattering feature image (MDSFI); 3) establishing the stacked denoising convolution sparse auto-encoder (SDCSAE) model based on the convolution-pooling structure to extend the denoising sparse auto-encoder (DSAE) into multi-dimension feature image domain for ship scattering characteristics coding and high level representation constructing, which will greatly benefit the ship classification capability with limited labeled ship samples. This study provides a new way for robust physical feature extraction in SAR ship classification, which can be widely used in both military and civil field, including national defense, traffic and environment.

英文关键词： feature extraction;target identification;deep learning;unsupervised feature learning;active microwave remote sensing