基于独立分量分析的高光谱遥感影像决策树分类

为解决高光谱遥感影像波段众多所带来的信息丰富与“维数灾难”间的矛盾并提高分类精度，针对传统特征选择方法信息损失大的缺陷，基于EO-1 Hyperion高光谱遥感影像，采用独立分量分析(ICA)和决策树分类(DTC)方法联合运作流程，开展影像的地物分类实验研究，提出了ICA-DTC模型。首先运用ICA方法对影像进行特征提取，并以所提取的独立分量特征及其他地理辅助要素组成分类指标集；继而选择适当的指标组合和阈值设定判别规则，建立DTC模型进行影像的地物分类；最后将分类结果与传统最大似然分类法进行比对。结果显示：从分类的总体精度看，前者可达89.34%，高出后者18.8%；从单一地物的分类精度看，前者仅水体的精度略低于后者，而其他11种地物的精度都高于后者。理论分析与实验结果均表明，ICA-DTC模型可有效提高复杂地形条件下的地物分类精度。

Decision tree classification of hyperspectral remote sensing imagery based on independent component analysis

Hyperspectral remote sensing imagery contains abundant spectral information because of its numerous bands,but it also causes the curse of dimensionality.How to resolve this conflict and improve the classification accuracy of hyperspectral remote sensing imagery is the major concern.Therefore,the thesis proposed ICA-DTC model that combined Independent Component Analysis(ICA) with Decision Tree Classifier(DTC) to research the hyperspectral imagery classification based on EO-1 Hyperion.First,ICA was applied to carry on the feature extraction on hyperspectral remote sensing imagery.Based on this,the characteristic components and other geography auxiliary elements were selected as test variables,the appropriate threshold was selected to set discriminating rule,and the DTC model was established to classify hyperspectral remote sensing imagery.Then the results obtained by this method were compared with that obtained by traditional maximum likelihood classification.The experimental results show that ICA can extract nonlinear characteristics from surface features well and ICA-DCT model can effectively improve the classification accuracy of surface features under complex terrain.In terms of the total classification accuracy,the former is up to 89.34%,18.8% higher than the latter.In terms of the classification accuracy of a single surface feature,the former is only slightly lower than the latter on water,while 11 other surface features are higher than the latter.