Keywords: Gray-level Co-occurrence Matrix, Fourier Transform, Texture Analysis
Neural Network, Multispectral Image
Abstract Two feature extraction methods, gray-level co-occurrence matrix and Fourier transform, are compared for land cover classification, which is viewed as texture of the image. Comparing results between these two texture features show that feature derived from the gray-level co-occurrence matrix give the better result than Fourier transform. With these features, supervised classification is carried out by the multi-layer perceptron (MLP) neural network using the back-propagation (BP) algorithm
1. Introduction
Texture is one of the most important defining characteristics of an image. It is characterized by the spatial distribution of gray levels in a neighborhood (Jain et al., 1995). In order to capture the spatial dependence of gray-level values which contribute to the perception of texture, a two-dimensional dependence texture analysis matrix are discussed for texture consideration. Since texture shows its characteristics by both each pixel and pixel values. There are many approaches using for texture classification. The gray-level co-occurrence matrix seems to be a well-know statistical technique for feature extraction. However, there is a different statistical technique using the absolute differences between pairs of gray levels in an image segment that is the classification measures from the Fourier spectrum of image segments.
Texture features derived from gray-level co-occurrence matrix and Fourier transform are used to be input data for unsupervised classification. By this experiment, multilayer perceptron (MLP) neural network using the back-propagation (BP) algorithm is chosen to be the classification algorithm.
2. Texture Features
Feature extractions acquired by this experiment are derived from the two methods. That are gray-level co-occurrence matrix and Fourier transform, which are the kinds of statistical methods. The more details of these texture analyses are shown by the following subheadings.
2.1 Gray-level Co-occurrence Matrix
Gray-level co-occurrence matrix is the two dimensional matrix of joint probabilities Pd,r(i,j) between pairs of pixels, separated by a distance, d, in a given direction, r. It is popular in texture description and based on the repeated occurrence of some gray level configuration in the texture; this configuration varies rapidly with distance in fine textures, slowly in coarse textures (Haralick et al., 1973).
Finding texture features from gray-level co-occurrence matrix for texture classification in this experiment are based on these criteria:
Energy:
Entropy:
Contrast: (typically k = 2, l = 1)
Homogeneity:
2.2 Fourier Transform
This classification measures from the Fourier spectrum of image segments require the calculation of the fast Fourier transform (FFT) for each segment and the definition of features in terms of the amplitudes of the spatial frequencies. The discrete Fourier transform F(un,vm) of a digitized image segment f(xj,yk) of size N1´N2 is defined by
where un and vm are the discrete spatial frequencies
xj and yk are pixel positions
The set of features based on the power spectrum consists of four statistical measures. If |F(j,k)| is the matrix containing the amplitudes of the spectrum and N is the number of frequency components then these measures are given by (Augusteijn et al. 1995)
