A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

Abstract

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.

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment

“…Hou et al [26] proposed an adaptive feature-weighted K-nearest-neighbors classification method by analyzing the influence of different features of natural images on the classification results; the experimental results indicated that the proposed algorithm could classify natural images quickly and accurately to meet the user-defined classification precision, time, and complexity requirements. To improve the generalization and learning abilities of SVM-based approaches, Zhao et al [27] classified remote sensing images using an improved compound kernel function. Bekaddour et al [28] integrated the k-means, learning vector quantization (LVQ) neural networks and SVM algorithms to classify multispectral satellite images.…”