SVM (Support Vector Machine) is a new kind of machine learning method , it can solve classification and regression
problems very successfully and accomplish classification with small sample incident perfectly. In this paper, the NPA is
proposed to compute the optimization problem to achieve the classification for hyperspectral remote sensing (RS) image
by "1 VS m" strategy and radial basis kernel function. Besides, a new method, the dual-binary tree + SVM algorithm is
proposed, to solve the mutil-class, high-dimensional(HD) problems of hyperspectral RS image. In the end, the test is
carried on the OMIS image. The comparative results of this algorithm with other methods are given, which shows that
our algorithm is very competitive, particularly for the small samples and non-equilibrium surface features. Both the
accuracy and speed of classification are improved greatly.
In the traditional BNC model, the relationship between the attributes are the same for all the instances of the class
variable C. BMN classifier is a generalized form of BNC, in the sense that it allows different relationships among
attributes for every values of the class variable, and provides a unique net structure for every object class. This paper
proposes Bayesian Multi-nets (BMN) Models based on the analysis of conditional mutual information(CMI) between
image features of different objects classes, and constructs BMN classifier for remote sensing images on the basis of
experiment. Classification accuracy of single objects in BMN classifier outperforms that of traditional BN, proves the
latent value of the proposed models in the classification of remote sensing images.