Support Vector Machines Method Of Aerial Image Segmentation

Image interpretation is a interest area and a bottle problem for photogrammetry and remote sensing. Support Vector Machines (SVM) is a hot research field in Machine Learning. SVM is a kind of novel machine learning method based on Statistical Learning Theory (SLT) proposed by Vapnik. SVM can better solve the learning problem of small sample. This paper proposes that the SVM is used in the classification and segmentation on aerial image. The purpose of the paper is to play foundation for researches on aerial image automatically interpretation.The main contents include the method of Support Vector Machines (SVM) on aerial image texture classification and image segmentation, the pre-selection sample method of Genetic Algorithm fuzzy - C mean, the application of Least Square Support Vector Machines(LS-SVM) and its sparseness, the compare of the SVM and the other methods on aerial image texture classification and image segmentation.(1) This paper proposes that the SVM is used in the aerial image texture classification and image segmentation. The results of SVM with mutli-nonlinear features are good .This paper researches the parameters (kernel, penalty parameter C) of SVM and the dimension of feature, which influence aerial image segmentation and classification. The selection of Kernel function less affects the correct results of aerial image texture classification. The best correct classification rate of different Kernel is similitude. But the effect of different Kernel is large for aerial image segmentation. The effect of C is large for aerial image segmentation and classification. The original value of C could use the method of cross-validation. The original value is adjusted to the best result. The more feature the more better results.Whereas the complexity of aerial image, the decision function occurs little difference which changes the classification of sample near super-plane. This paper proposes that hold the samples of ai = C for assure the segmentation correctness.This paper proposes the method of decision-tree SVM on two levels pyramid image, it could solve the segmentation problem of multi-classes objects on aerial image.(2) One of focus is the training method of SVM. The storage amount is interrelated with the number of samples in the learning process. This paper proposes the pre-selection sample method of Genetic Algorithm fuzzy - C mean. The result is that hold the samples nearing the supper plane, delete the samples far off the supper plane, decrease the training set and the storage.The reducing rate has little different for different of sample set. While the change of iteration number and SV number is little, the change of decision function is small, the memory is reduced through reducing sample set. At the same time, the proportion of SV has been increased, learning more validly focus on theSV's optimization.(3) The influence of penalty parameter C for classification and segmentation is large. People decide the value of C. LS-SVM avoid the selection the value of C. The segmentation results of LS-SVM are a little bad than SVM. The briefness of decision function is reached by the sparseness of LS-SVM.This paper proposes that the sparseness of LS-SVM is used in the aerial image segmentation. The results between sparseness and non-sparseness has little difference. Thus the decision function could be briefed by the sparseness of LS-SVM. The test speed could be improved.(4) Artificial Neural Network (ANN) is applications widely near years. The paper use SVM and ANN based on the same samples and features to classify and segment. The results indicate that SVM is better than ANN. The reason is ANN complete depends on the original power value, which don't have a robust decided method. People could find the best C of SVM in several times based on the experience.FCM is a general segmentation method. Because the features in the experiments aren't linear separate, FCM isn't successful even used supervised method, and the segmentation results of FCM are bad than SVM.