基于SVM遥感图像矿化信息提取试验

在讨论核函数的选择算法及优化的基础上,提出了一种将支持向量机(SVM)算法应用于遥感矿化信息提取的方法。并以TM遥感数据为试验样本,进行假彩色合成,将舍成图像的RGB值作为训练样本的特征向量,应用核函数选择算法和人为选择核函数方法,采用SVM算法对样本进行分类,试验表明选用径向基核函数所得的分类效果最好。认为对遥感影像作预处理后采用RGB值作为特征向量,应用支持向量机算法进行遥感矿化信息提取的方法能够获得较好的识别效果;应用LOO估算选择的核函数模型能够较好地逼近最佳值。

Testing for Extracting Mineralization Information from Remote Sensing ImageBased on Support Vector Machines

Based on the selection algorithm and corresponding optimization of the kernel function, we put forward a new approach for extracting mineralization information with Support Vector Machine(SVM) applied into remote sensing image in this paper. We synthesize false color using the TM remote sensing data as the test sample, and with the RGB value of the synthetic image as characteristic vector of train samples, with kernel function selection algorithm and man-selected kernel function method, the samples is classified through the SVM algorithm. The test indicated that the radial primary kernel function is the best classification method. It is considered that good recognition effect can be gained for extracting mineralization information with Support Vector Machine(SVM) applied into remote sensing image with the RGB value as the characteristic vector after pre-processing the remote sensing images. The kernel function model selected by LOO estimation can approach the best value satisfactorily.