三峡库区岩性智能分类研究

Research on lithology intelligent classification for Three Gorges Reservoir area

三峡库区岩体上方覆盖着厚实的土壤和茂密的植被,是高植被覆盖区,岩性信息弱,因此岩性识别和分类困难,没有成熟的方法可循。针对三峡库区进行岩性分析,选择三峡库区巴东城区作为研究区域,采用2000年5月成像的ETM+遥感影像,构造纹理、光谱、植被覆盖等17个分类因子,将遥感影像与地质图叠加,选取1101个样本点,采用决策树C4.5算法,挖掘出三峡库区巴东县处岩性的解译规则和知识,决策树的学习精度为96.6%,剪枝后精度为95.9%,规则提取的精度为93.1%,提取的规则置信度很高,并基于知识驱动和规则匹配实现了岩性的智能分类,分类精度较高为90.11%;将分类结果与IsoData方法、K-Means方法、马氏距离法、最大似然法、最小距离法、平行六面体方法等6种方法的分类结果进行比较,试验结果证明,决策树方法的分类结果最好,精度明显高于其他6种方法。

In the Three Gorges Reservoir area,there covers thick soils and flourish vegetation on the top of the rocks.The Three Gorges Reservoir area is an area which possesses flourish vegetation and poor lithology information.So it is difficult to classify and recognize the lithology in the Three Gorges Reservoir area,and on this aspect there are still no mature methods.In this paper lithology analysis is conducted focusing on the Three Gorges Reservoir area.Badong County in the Three Gorges Reservoir area is chosen as the study area.The ETM＋remote sensing image shot in May,2000 is adopted to establish 17 classification factors on the aspects of texture,spectrum and vegetation covering.The remote sensing image is piled up with the geological map;and 1 101 sample points are chosen.Decision tree algorithm C4.5 is adopted to mine the interpretation knowledge of lithology in Badong County.The study precisions of the trees before and after pruning are 96.6%and 95.9%respectively.The precision of the rule extraction is 93.1%;and the confidence values of the rules are very high.By knowledge driving and rule matching,intelligent litology classification is conducted with the classification precision of 90.11%.The classification results are compared with ones of the other 6 methods（IsoData,K-Means,Mahalanobis Distance,Maximum Likelihood,Minimum Distance and Parallelepiped） .The experimental results have shown that the decision tree method possesses the best classification results and the highest precision;and it is obviously superior to the other 6 methods.