自动化学报2016，Vol.42Issue(4)：593-604,12.DOI:10.16383/j.aas.2016.c150425
基于深度协同稀疏编码网络的海洋浮筏SAR图像目标识别
Research on Marine Floating Raft Aquaculture SAR Image Target Recognition Based on Deep Collaborative Sparse Coding Network
摘要
Abstract
Floating raft aquaculture is widely distributed in the offshore ocean of China. Since raft information cannot be obtained accurately in the visible remote sensing image, active imaging images acquired from synthetic aperture radar (SAR) are applied. However, oceanic SAR images are seriously contaminated by speckle noise, and effective features of SAR images are deficient, which make recognition difficult. In order to overcome these problems, a deep collaborative sparse coding network (DCSCN) is proposed to extract features and conduct recognition automatically. The proposed method extracts texture features and contour features from the pre-processed image firstly. Then, it segments the image into patches and learns features of each patch collaboratively through the DCSCN network. The optimized features are used for recognition finally. Experiments on the artificial SAR image and the images of Beidaihe demonstrate that the proposed DCSCN network can accurately obtain the area of floating raft aquaculture. Since the network can learn discriminative features and integrate the correlated neighbor pixels, the DCSCN network improves the recognition accuracy and has better performance in overcoming the contamination of speckle noise.关键词
合成孔径雷达/深度学习/稀疏自动编码器/浮筏养殖/目标识别Key words
Synthetic aperture radar (SAR)/deep learning/sparse auto-encoders/floating raft aquaculture/target recognition引用本文复制引用
耿杰,范剑超,初佳兰,王洪玉..基于深度协同稀疏编码网络的海洋浮筏SAR图像目标识别[J].自动化学报,2016,42(4):593-604,12.基金项目
国家自然科学基金(61273307,61301130),中国博士后面上基金(2014M551082),北戴河邻近海域典型生态灾害与污染监控海洋公益专项(201305003),海域使用动态监测和污染监测研究专项资助Supported by National Natural Science Foundation of China (61273307,61301130), the China Postdoctoral Science Founda-tion (2014M551082), the Public Welfare Project of Beidaihe Ma-rine Ecological Disasters and Pollution Monitoring (201305003) and the Research of Dynamic Monitoring and Pollution Moni-toring of Sea Area （61273307,61301130）
