An improved SVM method P‐SVM for classification of remotely sensed data

A support vector machine (SVM) is a mathematical tool which is based on the structural risk minimization principle. It tries to find a hyperplane in high dimensional feature space to solve some linearly inseparable problems. SVM has been applied within the remote sensing community to multispectral and hyperspectral imagery analysis. However, the standard SVM faces some technical disadvantages. For instance, the solution of an SVM learning problem is scale sensitive, and the process is time‐consuming. A novel Potential SVM (P‐SVM) algorithm is proposed to overcome the shortcomings of standard SVM and it has shown some improvements. In this letter, the P‐SVM algorithm is introduced into multispectral and high‐spatial resolution remotely sensed data classification, and it is applied to ASTER imagery and ADS40 imagery respectively. Experimental results indicate that the P‐SVM is competitive with the standard SVM algorithm in terms of accuracy of classification of remotely sensed data, and the time needed is less.