Support Vector Machine Research On Remote Sensing Image Interpretation

Image Understanding is the hotspot in computer research area. As the application research of Image Understanding on complex scenes, remote sensing Image Interpretation is aimed at correctly describing the contents in the image, and knowledge-based segmentation is an essential part of a remote sensing Image Interpretation system. As a recognition method based on Statistical Learning Theory, SVM classifies samples by constructing discriminant function in feature space, and have been a popular classifier for generalized object recognition. The paper studies the SVM methods and promotes Multi-SVM model so as to realize a remote sensing image segmentation method through classifying patterns, as primary work of remote sensing Image Interpretation.This paper includes the following contents:(1) We study the learning theory and classification mechanism of SVM, discuss the kernel points of the SVM algorithm, and summarize the modified SVM models, forming a comparatively complete system of SVM.(2) We introduce the construction mechanism of Multi-SVM, and compare DTSVM with DAGSVM through data classification experiments; we construct a Hierarchical Clustering DTSVM model by introducing hierarchical clustering for designing decision tree, and mixture of kernels for data mapping, and then verify the validation of this model through experiments on standard dataset, so as to lay a theoretical foundation for model selection.(3) We focus on the feature of remote sensing image and design the feature extraction method, annotate the semantic objects to form feature dataset, and accomplish the DTSVM based segmentation of remote sensing image through training Hierarchical Clustering DTSVM model.(4) We summarize the development of remote sensing Image Interpretation, realize knowledge-based segmentation for remote sensing image, and evaluate the results via popular criteria. The results involve semantic knowledge, and therefore provide information for further interpretation of remote sensing image.