Research On Hyperspectral Remote Sensing Image Classification Based On Support Vector Machine

With the rapid development of the hyperspectral remote sensing data acquisition technology, it has become the most important aspect to process and analyze hyperspectral data with high performance. As the most effective algorithm of the Statistical Learning Theory (SLT), Support Vector Machine (SVM) has the small sample study, high-dimensional space, nonlinear, etc., and becomes the hot issue because of its superiority of the hyperspectral remote sensing image classification. In this paper, SVM theory and its improvement were studied in detail based on the statistical learning concept, and some improvements of SVM algorithms(such as multi-class SVM designed, wavelet Kernel SVM, multiple kernel functions) were successfully applied in hyperspectral remote sensing data classification. The main results are as follows:Four common kernel functions of SVM were analyzed and compared. And we compared SVM with some typical classifiers such as Minimum Distance classifier (MDC), Radial Basis Function Neural Network (RBFNN) classifier, Spectral Angle Mapper(SAM), Maximum Likelihood classifier(MLC). SVM could effectively overcome the Hughes phenomenon with inadequate samples. Overall, it is concluded that the classification speed of SVM are faster than RBFNN, and the accuracy of the SVM classifiers are higher than MDC, RBFNN, SAM, MLC Classifier.By executing the experiments of the feature extraction algorithms including Principal Component Analysis (PCA), Maximum Noise Fraction (MNF), Independent Component Analysis(ICA), feature extraction after correlation coefficient grouping, derivative spectral analysis and so on, it indicates that the SVM model has the fluctuation accuracy with feature dimension and PCA achieves the best accuracy for SVM classification. According to the experiments, it is effective to choose the PCA as the hyperspectral data feature extraction for the classification.According to the SVM theory and the separability measure of hyperspectral data, a novel binary tree multi- class SVM classifier based on separability between classes was put forward. It indicates that the novel binary tree classifier has highest accuracy than the other multi-class SVM classifiers and some traditional classifiers (SAM and MDC).With the study on the SVM theory based on reproducing kernel Hibert Space (RKHS) and wavelet analysis, the wavelet SVM (WSVM) classifier based on wavelet kernel functions was constructed. In the experiments, the WSVM classifier demonstrated more accurate results when it was using Coiflet wavelet Kernel function. Compared with some traditional classifiers (SAM&MDC) and classic kernel (Radial Basis Function kernel) of SVM, it indicates that wavelet kernel SVM classifier is most accurate.Usually, remote sensing image classifiers are limited in terms of the ability to combine spectral features with spatial features. Multiple kernel classifiers, however, are capable to integrate spectral features with spatial or structural features by using multiple kernels for spectral and spatial features and summating them for final outputs. The results show that more accurate classification results can be obtained by integrating the spectral and wavelet texture information, using the multiple kernel SVM classifiers. Moreover, when multiple kernel SVM classifier was being adopted, the highest accuracy can be provided by the combination of the first four principal components from PCA with textural features.Furthermore, a method is proposed for the classification of hyperspectral data with high spatial resolution by using SVM with multiple kernels. In the approach, morphological profile (MP) was used for hyperspectral data classification. The results show that by the integrating the spectral features and MP features, the multiple kernel SVM classifiers obtain more accurate classification results than sole-kernel SVM classifier. Moreover, when the multiple kernel SVM classifier was being used, the combination the first seven principal components derived from Principal Components Analysis (PCA) and MP can provide the highest accuracy (91.0%).