Recognition of remote sensing target based on support vector machine

The recognition results of image targets by such existing methods as neural network and Euclid distance methods are not satisfactory for shaded image or 3-D rotation image. We present an improved SVM (support vector machine) method for the recognition of such images. First, remote sensing images are processed into binary images. Then, the binary images are normalized according to the kernel function used in SVM (where the kernel function is Gaussian, the range of normalization is 0 to 0.02), and the normalized image targets are divided into two sets: one is for training, and the other is for testing. After that, SVM is trained by the training set. Finally, the trained SVM is used to test the testing set. We find that the normalization process is crucial for the application of SVM.