Classification Of Remote Sensing Image Based On SVM With Multi-feature Described

Remote sensing image is an important data source of water conservancy information. Extracting the required information from the remote sensing image is a key step. With the diversification of the means to acquire image data, the number of remote sensing image data is increasing. How to make the classification of remote sensing image with more efficiency is critical. This paper focuses on automatic classification of remote sensing image. O n the basis of reading the domestic and foreign literature, several aspects concerning the classification was conducted.It was shown that, only a few facts were taken in account in the classification of remote sensing image, and the methods used by scientists were not comprehensive enough. We think that the intelligent algorithms and multi- feature description should be combined with each other. At the same time, based on research status of the support vector machine(SVM) in this field, combining of the spectral characteristics of images and texture features described by a variety of methods as the input vectors, the SVM classifier was proposed.The primary theories and algorithms were introduced, and the parameters selection algorithms were discussed in detail. According to the generalization error bounds of SVM, the small sample properties of SVM and its effect on the complexity control of learning model was analyzed. At the same time, the maximum likelihood estimation, nearest neighbor(NN), K-nearest neighbor(K-NN), naive bayesian, support vector machine classification of remote sensing image, ware researched and compared on their accuracy, efficiency and applicable conditions.As texture description method, basic introduction and comparison of the gray histogram, Gabor wavelet, discrete Fourier circle sampling and discrete wavelet decomposition were presented. According to the derived process of Gabor wavelet filter, the basic guiding principle of the scale parameter selection was proposed. Through the improvement of DFT average circle sampling histogram method, the discrete Fourier circle sampling method was improved.Combining with multi- feature description and SVM classification algorithm, based on C++ language, Lib SVM, Open CV, Free Image, SQLite, Q T and other open source tools, a set of experimental system has been developed. Based on a piece of Landsat8 OLI image(a small part of northwest of Zhengzhou) as an example data, a series of experiments were conducted.Our experiments showed that, SVM classification algorithm proposed by this study has a much higher precision than the MLE, K-NN and naive Bayes. Each of the four kinds of texture description algorithm has distinguishing ability, especially the Gabor wavelet and DFT average circle sampling histogram methods. Compared with image classification by spectral characteristics and multi- features combined in SVM could improve the classification accuracy by 10% and the overall classification accuracy up to 96.2%. Combined with a variety of texture description algorithm, the classification accuracy of SVM image could be improved further. The experiments also showed that, combining high distinction texture description algorithm with low texture description algorithm, the classification accuracy would be higher than that of the combination of a variety of higher distinction description algorithm. The analysis and explanation of this phenomenon was carried on from the perspective of the model complexity control.