Abstract：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.
林志垒 晏路明. 基于独立分量分析的高光谱遥感影像决策树分类[J]. 计算机应用, 2012, 32(02): 524-527.
LIN Zhi-lei YAN Lu-ming. Decision tree classification of hyperspectral remote sensing imagery based on independent component analysis. Journal of Computer Applications, 2012, 32(02): 524-527.
2012, Vol. 32 
