Abstract. The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.
On the base of studying and analyzing the Globus Toolkit2.4 platform and remote sensing technology, this article uses the grid platform Globus Toolkit2.4 and the mode of parallel processing, develops a method of geometric correction for processing remote sensing images based on grid. It provides an approach for solving problems caused by RS image's characteristics of complexity and time consuming.
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