Classification of multispectral remote sensing image using Kernel Principal Component Analysis and neural network

Abstract

ABSTRACT A method combined Kernel Principal Component Analysis (KPCA) with BP neural network is proposed for multispectral remote sensing image classification in this paper. Firstly, the KPCA transformation including Gaussian KPCA and polynomial KPCA is carried out to get the former three uncorrelated bands containing most information of the TM images with seven bands. Secondly, BP neural network classification is executed using the three bands data after KPCA transformation. For testifying, both the classical PC A and the KPCA are applied to the multispectral Landsat TM data for feature extraction. The results demonstrate that the method proposed in this paper can improve the classification accuracy compared with that of principal component analysis (PCA) and BP neural network. Keywords: Kernel principal component analysis, BP neural network, Multispectral remote sensing, Classification 1. INTRODUCTION Feature reduction in a remote sensing dataset is often desirable to decrease the processing time required to perform a classification. Principal component analysis (PCA) is a common method for image enhancement and compression. PCA maximizes the projection variance in the previous r vectors (r refers to the number of dimension which need to be reduced) according to search this r orthogonal eigenvectors (corresponding to previous r maximal eigenvalues) [1]. However, PCA method is a linear mapping algorithm in nature; it only extracts the linear features but loss of the non-linear features. Therefore, kernel principal component analysis (KPCA) is been put forward to deal with the non-linear problems in some re ferences [2, 3]. Recently, kernel-based learning algorithms which had been proved to be a promising method for tackling nonlinear systems have attracted much attention of researcher s in the field of machines learning. KPCA is applied to many fields including failure detection in waste water treatment plants [4–6], data denoising [7], recognition of ha ndwritten digits [8] and classification of genetic data [9] an so on. In recent years, there are some papers using KPCA method for feature extraction in image processing. A kernel machine-based discriminant analysis method presented by Juwei Lu et al deals with the nonlinearity of the face patterns' distribution [11]. The application of KPCA for dimension reduction on remote sensin g datasets with inherent non-linear structure was present by John Tan et al [12]. A method combined KPCA and SAM provided by Zhang Youjing et al. has been shown to yield high classification accuracy for city’s vegetation [13]. KPCA feature extraction based on Mahalanobis distance Fuzzy C-Means genetic algorithm provided by Chang Ruichun has been shown to yield high classification accuracy in extracting desertification nonlinear feature [14].