基于最小二乘支持向量机和高分辨率遥感影像的大尺度区域岩性划分

基于大尺度区域分割的理念,提取高分辨率遥感图像中与岩性相关的纹理、形状、光谱信息,利用最小二乘支持向量机(LS-SVM)在非线性预测中的优势,对研究区地质岩性进行识别。首先对高分辨率图像中与岩性相关的光谱、纹理、形状、高程等特征信息进行样本选取,选取过程中以图像的纹理为主要特征信息,同时以J-M距离、转换分类度为依据选取最优特征空间,采用因子分析变换降维对特征空间进行压缩,实现特征信息最优化;然后对已知样本进行训练,建立分类模型,评价模型精度;最后利用模型对研究区进行岩性划分,并进行分类后处理。研究结果表明:基于LS-SVM的分类方法在利用高分辨率遥感图像岩性识别中表现良好,为地质岩性分类提供了一种新的方法和手段;加入纹理等信息后的LS-SVM分类模型更加利于岩性的判别。

Lithology division for large-scale region segmentation based on LS-SVM and high resolution remote sensing images

Based on the concept of large-scale region segmentation,extraction of texture,shape,spectral information of high resolution remote sensing image associated with the lithology and the advantages of least squares-support vector machines(LS-SVM) in the non-linear prediction were used in the geological lithology identification.Firstly,the samples of spectral,texture,shape and altitude information which are relevant to lithology in the high resolution remote sensing images are selected.During the course of selecting,the image s texture is the main characteristic information.In the meanwhile,the chosen optimization feature space is based on the J-M distance and the degree of conversion classification.The feature space is compressed by using factor analysis and transformation dimension reduction,so that the characteristic information can be optimized.Then,known samples are trained,and classification model is developed to evaluate model accuracy.Finally,the model was used to divide the study area s lithology and process classified objects.The classification method based on LS-SVM performs well in the high-resolution remote sensing images lithological identification,and provides a new method and means for the classification of geological lithology.LS-SVM classification model is more conducive in lithology identification after adding texture.