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应用科学学报 ›› 2015, Vol. 33 ›› Issue (1): 21-31.doi: 10.3969/j.issn.0255-8297.2015.01.003

• 信号与信息处理 • 上一篇    下一篇

基于Shearlet 变换和Krawtchouk 矩不变量的河流SAR 图像分割

  

  1. 1. 南京航空航天大学电子信息工程学院，南京210016
    2. 长江水利委员会长江科学院武汉市智慧流域工程技术研究中心，武汉430010
    3. 黄河水利委员会黄河水利科学研究院水利部黄河泥沙重点实验室，郑州450003
    4. 哈尔滨工业大学城市水资源与水环境国家重点实验室，哈尔滨150090
    5. 南京水利科学研究院港口航道泥沙工程交通行业重点实验室，南京210024
  • 收稿日期:2014-09-13 修回日期:2014-12-22 出版日期:2015-01-30 发布日期:2015-01-04
  • 通信作者: 吴一全，教授，博导，研究方向：遥感图像处理与分析、目标检测与识别、视觉检测与图像测量、视频编码与视频处理、生物特征识别与数字水印等，E-mail：nuaaimage@163.com
  • 基金资助:

    国家自然科学基金(No.60872065)；长江科学院开放基金(No. CKWV2013225/KY)；水利部黄河泥沙重点实验室开放基金(No.2014006)；城市水资源与水环境国家重点实验室开放基金(No.LYPK201304)；港口航道泥沙工程交通行业重点实验室开放基金；江苏高校优势学科建设工程基金

Segmentation of SAR Image of Rivers Based on Shearlet Transform and Krawtchouk Moment Invariants

  1. 1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
    2. Engineering Technology Research Center of Wuhan Intelligent Basin, Changjiang River Scientific Research Institute, Changjiang Water Resources Commission, Wuhan 430010, China 3. Key Laboratory of the Yellow River Sediment of Ministry of Water Resource, Yellow Institute of Hydraulic Research, Yellow River Water Resources Commission, Zhengzhou 450003, China
    4. State Key Laboratory of Urban Water Resource and Environment, Harbin Institute Technology, Harbin 150090, China
    5. Key Laboratory of Port, Waterway and Sedimentation Engineering of the Ministry
    of Transport, Nanjing Hydraulic Research Institute, Nanjing 210024, China
  • Received:2014-09-13 Revised:2014-12-22 Online:2015-01-30 Published:2015-01-04

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703

被引次数

1

摘要： 合成孔径雷达(synthetic aperture radar, SAR)图像分割是河流检测与识别的关键步骤，为了进一步提高河流SAR图像分割的准确性，提出一种基于Shearlet变换、Krawtchouk矩不变量及模糊局部信息C 均值聚类的河流SAR 图像分割方法. 首先，对河流SAR图像进行Shearlet 分解，提取其纹理特征，构成特征向量的前半部分；然后，计算河流SAR 图像的Krawtchouk 矩不变量，作为其形状特征，构成特征向量的后半部分；最后，利用模糊局部信息C 均值算法依照上述特征向量进行聚类，由此得到河流SAR 图像分割结果. 大量实验结果表明，与近年来提出的脉冲耦合神经网络结合最大方差比准则分割法、Gabor 小波变换结合模糊C 均值聚类分割法、FLICM 聚类分割法相比，所提出的方法在主观视觉效果以及客观定量评价指标误分割率上均有明显优势，且分割河流SAR 图像更加准确.

关键词: 河流检测, SAR图像分割, Shearlet变换, Krawtchouk矩不变量, 模糊局部信息C均值聚类

Abstract: Segmentation of synthetic aperture radar (SAR) images is a key procedure in river detection and recognition. To further improve accuracy of SAR image segmentation for rivers, a segmentation method is proposed based on Shearlet transform, Krawtchouk moment invariants, and fuzzy local information C-means (FLICM). The SAR image is first decomposed with Shearlet transform, and its texture features are extracted as the first part of the feature vector. Krawtchouk moment invariants of the image are then calculated to obtain corresponding shape features used as the second part of the feature vector. Finally,
the image is clustered based on FLICM algorithm using the extracted feature vector. Thus segmentation of the river SAR image is obtained. A large number of experiments are performed. The results are compared with several recently proposed methods based on pulse coupled neural network (PCNN) combined with maximum variance ratio, and Gabor wavelet transform combined with fuzzy C-means (FCM) and FLICM clustering. It has been shown that the proposed method has clear advantages both in subjective visual effects and in terms of objective evaluation indicator such as segmentation error rate. The method can
provide river segmentation with better accuracy.
 

Key words: river detection, SAR image segmentation, Shearlet transform, Krawtchouk moment invariant, fuzzy local information C-means clustering (FLICM)

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