合成孔径雷达（SAR）图像由于受到相干斑点噪声的影响，使得其高精度的分类算法研究受到极大的挑战。为了提高SAR图像分类的性能，本文根据SAR图像的成像机理和统计特性，通过灰度共生矩阵特征的提取，结合纠错编码，构造了一种SAR图像分类的AdaBoost改进算法。实验结果表明，该算法得到较好的分类结果，分类精度得到了显著的提高。%Owing to speckle noise, the research of high-precision classification of SAR (Synthetic Aperture Radar) images is a big challenge. According to the imaging mechanism and the statistical properties of SAR images, this paper proposes a clas-sification algorithm based on improved AdaBoost to improve the classification performance of SAR images. In this classification algorithm, the gray level co-occurrence matrix is used to extract the features and error correcting output code is introduced. Experimental results show that the proposed classification algorithm can obtain a better classification result and the accuracy is significantly improved.
展开▼
