吴一全, 陶飞翔, 曹照清. 基于Log-Gabor小波和Krawtchouk矩的遥感图像分类[J]. 武汉大学学报 ( 信息科学版), 2016, 41(7): 861-867. DOI: 10.13203/j.whugis20140234
引用本文: 吴一全, 陶飞翔, 曹照清. 基于Log-Gabor小波和Krawtchouk矩的遥感图像分类[J]. 武汉大学学报 ( 信息科学版), 2016, 41(7): 861-867. DOI: 10.13203/j.whugis20140234
WU Yiquan, TAO Feixiang, CAO Zhaoqing. Remote Sensing Image Classification Based on Log-Gabor Wavelet and Krawtchouk Moments[J]. Geomatics and Information Science of Wuhan University, 2016, 41(7): 861-867. DOI: 10.13203/j.whugis20140234
Citation: WU Yiquan, TAO Feixiang, CAO Zhaoqing. Remote Sensing Image Classification Based on Log-Gabor Wavelet and Krawtchouk Moments[J]. Geomatics and Information Science of Wuhan University, 2016, 41(7): 861-867. DOI: 10.13203/j.whugis20140234

基于Log-Gabor小波和Krawtchouk矩的遥感图像分类

Remote Sensing Image Classification Based on Log-Gabor Wavelet and Krawtchouk Moments

  • 摘要: 为了进一步提高遥感图像分类的精度,提出了一种基于Log-Gabor小波和Krawtchouk矩的遥感图像分类算法。首先利用Log-Gabor小波对遥感图像进行多方向、多分辨率滤波,提取遥感图像的纹理特征;同时计算遥感图像的Krawtchouk矩不变量,作为遥感图像的边缘形状特征,并与基于Log-Gabor小波提取的纹理特征构成完整的特征向量;最后依据所提取的特征向量利用支持向量机(support vector machine,SVM)分类器对待分类图像进行分类,得到最终的遥感图像分类结果。实验结果表明,与近年来提出的基于Gabor小波、基于Log-Gabor小波、基于Krawtchouk矩等3种遥感图像分类算法相比,本文算法在主观视觉效果和分类精度等客观定量评价指标上都有了明显的改善,是一种行之有效的遥感图像分类算法。

     

    Abstract: To further improve the accuracy of remote sensing image classification, a classification algorithm of remote sensing image based on Log-Gabor wavelet and Krawtchouk moments is proposed in this paper. Firstly, multi-direction and multi-resolution filtering is performed on a remote sensing image by Log-Gabor filter to extract texture features of the remote sensing image. Meanwhile Krawtchouk moments invariants of the remote sensing image are calculated to serve as edge shape features of the remote sensing image. Thus a complete feature vector is constructed with the texture features extracted by Log-Gabor wavelet. Finally the remote sensing image is classified according to the extracted feature vectors by supporting vector machine. The classification result of remote sensing image is obtained. A large number of experimental results show that, compared with three recent classification algorithms of remote sensing image such as the algorithm based on Gabor wavelet, the algorithm based on Log-Gabor wavelet and the algorithm based on Krawtchouk moments, the proposed algorithm has a significant improvement in the subjective visual effect and objective quantitative evaluation index such as classification accuracy. It is a kind of effective classification algorithm of remote sensing image.

     

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