基于SVM的高维多光谱图像分类算法及其特性的研究

High Dimensional Multi-spectral Image Classification by SVM and Its Characteristic Analysis

针对传统模式分类算法在处理高维多光谱图像时面临的困难，文章把支持向量机（Support Vector Machine，SVM）用于高维多光谱图像分类，有效地减弱了Hughes现象，获得了比传统方法更好的分类精度。研究了高维多光谱图像分类中SVM的分类性能与训练样本数目和数据维数之间的关系。实验结果表明，与传统模式分类方法相比，SVM具有分类精度高、推广性强的优点，尤其是当学习样本数目较少、数据维数高时，SVM的优势更加明显。

More and more difficulties are encountered when using traditional algorithms (including MD, ML, NNC, RBFNNC) to classify high dimension multi-spectral image. In order to overcome these limitations, support vector machine (SVM) is used to classify the high dimension image. The Hughes phenomenon is mitigated greatly, which is notorious in classification of high dimension data with limited training samples by traditional classifier. So the classification accuracy of SVM is better than traditional ones. The relationship between the classification performance of SVM and training set, data dimension is studied. Experiment results show that the generalization ability of SVM is strong, and its classification accuracy is better than traditional algorithms, no matter whether the training set is huge or small. Advantages of SVM are much more notable when the training set is small and the data dimension is high.