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基于深度协同稀疏编码网络的海洋浮筏SAR图像目标识别

耿杰 范剑超 初佳兰 王洪玉

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. 自动化学报, 2016, 42(4): 593-604. doi: 10.16383/j.aas.2016.c150425
引用本文: 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. 自动化学报, 2016, 42(4): 593-604. doi: 10.16383/j.aas.2016.c150425
GENG Jie, FAN Jian-Chao, CHU Jia-Lan, WANG Hong-Yu. Research on Marine Floating Raft Aquaculture SAR Image Target Recognition Based on Deep Collaborative Sparse Coding Network. ACTA AUTOMATICA SINICA, 2016, 42(4): 593-604. doi: 10.16383/j.aas.2016.c150425
Citation: GENG Jie, FAN Jian-Chao, CHU Jia-Lan, WANG Hong-Yu. Research on Marine Floating Raft Aquaculture SAR Image Target Recognition Based on Deep Collaborative Sparse Coding Network. ACTA AUTOMATICA SINICA, 2016, 42(4): 593-604. 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

doi: 10.16383/j.aas.2016.c150425
基金项目: 

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

国家自然科学基金 61273307, 61301130

中国博士后面上基金 2014M551082

详细信息
    作者简介:

    耿杰, 大连理工大学电子信息与电气工程学部博士研究生. 主要研究方向为SAR图像处理, 模式识别.E-mail:gengjie@mail.dlut.edu.cn

    初佳兰, 国家海洋环境监测中心工程师. 主要研究方向为海域动态卫星遥感应用.E-mail:jlchu@nmemc.org.cn

    王洪玉, 大连理工大学电子信息与电气工程学部教授. 主要研究方向为图像处理, 模式识别和无线传感器网络.E-mail:whyu@dlut.edu.cn

    通讯作者:

    范剑超, 国家海洋环境监测中心, 大连理工大学电子信息与电气工程学部副研究员. 主要研究方向为神经网络, 模式识别和遥感图像处理. 本文通信作者.E-mail:fjchaonmemc@163.com

Research on Marine Floating Raft Aquaculture SAR Image Target Recognition Based on Deep Collaborative Sparse Coding Network

Funds: 

the Public Welfare Project of Beidaihe Marine Ecological Disasters and Pollution Monitoring 201305003

National Natural Science Foundation of China 61273307, 61301130

the China Postdoctoral Science Foundation 2014M551082

More Information
    Author Bio:

    Ph. D. candidate at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. His research interest covers SAR image processing and pattern recognition

    Engineer at National Marine Environment Monitoring Center. Her research interest covers sea dynamic surveillance remote sensing application

    Professor at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. His research interest covers image processing, pattern recognition, and wireless sensor networks

    Corresponding author: FAN Jian-Chao Associate professor at National Marine Environment Monitoring Center and Dalian University of Technology. His research interest covers neural network, pattern recognition, and remote sensing image processing. Corresponding author of this paper
  • 摘要: 浮筏养殖广泛存在于我国近海海域, 可见光遥感图像无法完全准确地获取养殖目标, 而基于主动成像的合成孔径雷达(Synthetic aperture radar, SAR)遥感图像能够得到养殖目标, 因此采用SAR图像进行海洋浮筏养殖目标识别. 然而, 海洋遥感SAR图像包含大量相干斑噪声, 并且SAR图像特征单一, 使得目标识别难度较大. 为解决这些问题, 提出一种深度协同稀疏编码网络(Deep collaborative sparse coding network, DCSCN)进行海洋浮筏识别. 本文方法对预处理后的图像先提取纹理特征和轮廓特征, 再进行超像素分割并将同一个超像素块特征组输入该网络进行协同表示, 最后得到有效特征并分类识别. 通过人工SAR图像和北戴河海域浮筏养殖SAR图像的实验验证所提模型的有效性. 该网络不仅具有优异的特征表示能力, 能够获得更适合分类器的特征, 而且通过近邻协同约束, 有效抑制相干斑噪声影响, 所以提高了SAR图像目标识别精度.
  • 图  1  堆叠自动编码器结构图

    Fig.  1  The structure of SAE

    图  2  非下采样轮廓波变换

    Fig.  2  Nonsubsampled contourlet transform

    图  3  基于深层协同稀疏编码网络算法流程图

    Fig.  3  The flow chart of the proposed algorithm

    图  4  深度协同稀疏编码网络结构图

    Fig.  4  The structure of deep collaborative sparse coding network

    图  5  人工SAR 图像数据

    Fig.  5  The arti-cial SAR data

    图  6  DCSCN 网络参数设置

    Fig.  6  Parameter setting of the DCSCN

    图  7  人工SAR 图像目标识别结果

    Fig.  7  Target recognition results of the arti-cial SAR image

    图  8  北戴河区域SAR 数据

    Fig.  8  The original SAR data of Beidaihe are

    图  9  SAR 图像1 浮筏识别结果

    Fig.  9  Target recognition results of the -rst SAR image

    图  10  SAR 图像2 浮筏识别结果

    Fig.  10  Target recognition results of the second SAR image

    表  1  各个算法在人工SAR 图像上识别结果对比

    Table  1  Recognition performance comparison of di®erent algorithms on the arti-cial SAR image

    算法OA(%)κ计算效率(s)
    SVM[25]83.574±0.1320.585±0.006166.623±1.854
    SOMP[26]92.803±0.5880.775±0.019568.204±3.376
    SAE[24]95.261±0.3750.858±0.011312.044±2.975
    Lasso-Pooling[27]97.972±0.1830.938±0.005757.605±6.334
    DCSCN99.457±0.0790.983±0.002192.630±4.481
    下载: 导出CSV

    表  2  各个算法在SAR 图像1 浮筏识别结果对比

    Table  2  Recognition performance comparison of di®erent algorithms on the -rst SAR image

    算法OA(%)κ计算效率(s)
    SVM[25]79.804±0.3750.653±0.004528.170±4.266
    SOMP[26]86.243±0.6150.708±0.00712 411.229±62.799
    SAE[24]81.960±0.3920.686±0.0057 963.060±29.862
    Lasso-Pooling[27]86.925±0.0730.692±0.00615 796.110±79.618
    DCSCN89.046±0.4330.759±0.005458.050±2.145
    下载: 导出CSV

    表  3  各个算法在SAR 图像2 浮筏识别结果对比

    Table  3  Recognition performance comparison of di®erent algorithms on the second SAR image

    算法OA(%)κ计算效率(s)
    SVM[25]87.714±1.0260.693±0.007794.139±7.148
    SOMP[26]93.261±0.0680.821±0.0216 432.545±39.582
    SAE[24]93.179±0.2400.816±0.0031 245.995±16.942
    Lasso-Pooling[27]93.614±0.1750.832±0.0428 381.801±44.702
    DCSCN98.762±0.1720.966±0.003268.498±1.633
    下载: 导出CSV
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