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.

[1]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[2]  Hassiba Nemmour,et al.  Multiple support vector machines for land cover change detection: An application for mapping urban extensions , 2006 .

[3]  Guobin Zhu,et al.  Classification using ASTER data and SVM algorithms;: The case study of Beer Sheva, Israel , 2002 .

[4]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[5]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[6]  Jordi Inglada,et al.  Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features , 2007 .

[7]  J. Platt.  Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[8]  Klaus Obermayer,et al.  Support Vector Machines for Dyadic Data , 2006, Neural Computation.

[9]  Paul M. Mather,et al.  Support vector machines for classification in remote sensing , 2005 .

[10]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Ryan M. Rifkin,et al.  In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..

[12]  Alexander Siegmund,et al.  Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data , 2003 .