In order to raise the intelligent level and improve cooperative ability of grid. This paper proposes an agent oriented
middleware, which is applied to the traditional OGSA architecture to compose a new architecture named CIG
(Cooperative Intelligent Grid) and expounds the types of cooperative processing of remote sensing, the architecture of
CIG and how to implement the cooperation in the CIG environment.
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.