Research on Classification of Hyperspectral Remote Sensing Imagery Based on BDT-SMO and Combined Features

There are some prevalent problems in the classification of hyperspectral remote sensing imagery currently, such as many bands, large amount of data, high proportion of mixed pixels and lower spatial resolution and so no. In order to solve the above problems, the sequential minimal optimization (SMO) algorithm is researched, and a supervised classification method based on binary decision tree SMO (BDT-SMO) algorithm and spectrum-texture combined features is proposed to improve the accuracy and efficiency of hyperspectral remote sensing imagery classification. The higher spatial resolution imagery (ALI) and hyperspectral imagery (Hyperion) which have been acquired from the same sensor (EO-1) in the same time are used as the experimental data and implemented geometric correction and pixel-level fusion. Extract the spectral features and textural features of the ground objects from the fusion images, combine the features above and train the BDT-SMO multi-class classifier based on separation degree. The classifier is used for the land-use status classification of experimental areas. Select two different sets of samples which are based on spectral features and spectrum-texture combined features, and use the four different methods of maximum likelihood, BP neural network, BDT-SVM and BDT-SMO to train the two different sets of samples above separately. Experimental results show that the BDT-SMO classification method based on combined features can improve the efficiency and accuracy of land-use status classification effectively and has better generalization ability.