Class Modeling and Remote Sensing Image Classification Using Selected Spectral and Spatial Features

To improve the quality of the thematic map generated from spectral data, spatial information can be taken into account. Texture features, such as contrast, correlation, energy and homogeneity, can be combined with the original spectral data so that more detailed presentation of each class’ characteristics is obtained. On the other hand, we are facing a very high dimensional data set to handle. In this paper, classification using a large number of features is addressed with strong emphasis on the need of feature selection and feature extraction. Texture features and associate parameters are examined in terms of correlations between them and their contributions toward class separability. Class data normality test is introduced using skewness and kurtosis before the Bhattacharyya distance is applied for effective feature selection. A progressive two-class decision classifier is adopted for a flexible multistage classification. Experimental results show a significant improvement by the pair-wise classification using properly selected features. Introduction Multispectral or hyperspectral data record the reflectance of various earth cover types at different wavelengths. This spectral information has been widely used in pixel labeling [1]. However, such pixel-based classification may introduce ‘pepper and salt’ appearance on the generated thematic map. To overcome this problem, spatial information can be taken into account. Classification using texture features have been studied in recent years [2-5]. It is expected the texture measures, such as contrast, correlation, energy and homogeneity, can be combined with the original spectral data so that more detailed presentation of each class’ characteristics is obtained. On the other hand, we are facing a very high dimensional data set to handle. A number of texture measures with various spatial distances in different directions can be generated from each spectral band. The resulting high dimensional data can experience the Hughes phenomenon [6] or the curse of dimensionality. Feature reduction is required to reduce the computational cost and, more importantly, to improve the robustness of the class models established. In this paper, texture features and associate parameters are examined in terms of correlations between them and their contributions toward class separability. Class data normality test is introduced using skewness and kurtosis before the Bhattacharyya distance is applied for effective class separability based feature selection. A binary classification scheme called progressive twoclass classification is adopted for pixel labeling. Experimental results show a significant improvement by the pair-wise classification using properly selected features. Test Data Set A subset of Landsat Enhanced Thematic Mapper Plus (ETM+) data recorded over Canberra region, Australia, on 25 April 2001 has been used for demonstrating the developed procedure. The area consists of a lake, city centre and surrounding suburbs, and bushland. The ETM+ sensor records data in six spectral bands in the visible and middle infrared range with the spatial resolution of 30x30 m. It also has an image in the thermal infrared wavelength with lower spatial resolution. This channel is not used in this work. The radiometric resolution is 8 bits. Texture Feature Generation Texture in an image is determined by the pattern of the brightness values within a neighborhood. The Gray Level Cooccurrence Matrix (GLCM) [7] has been used widely to present the spatial relationships of pixels. It calculates how often a pixel with the gray value i occurs in a neighboring pixel with the value j. The neighbors can be on its immediate right, above, or in other directions. They also can be two or more pixels away. This parameter is referred as spatial distance in this paper. The element glcm(i, j) is simply the sum of the number of times that the pixel with value i occurred in the neighbor with value j in the input image. Several image texture statistics can be derived from the normalized glcm(i, j), ie. glcm (i, j) = 1 i, j ∑ , as detailed below [8]. Contrast: It is also called variance and inertia. It measures the local variations in the image. Correlation: It measures the linear dependency of brightness of neighboring pixels at the spatial distance defined. Energy: It is also known as uniformity or the angular second moment. Homogeneity: It measures the closeness of the distribution of elements in the GLCM to the GLCM diagonal. Above texture measures can be generated for each pixel of an original spectral band using the glcm(i, j) calculated within a local window. The new bands/features can then be stacked togather with the original spectral bands. The window size selection needs to consider the spatial resolution of the image and the distance of neighborhood defined in generating GLCM. With the testing data set, since it is prohibitive to calculate a GLCM for the full dynamic range of 0 to 255, they are scaled to 32. 192 texture bands were generated with following parameters. No. of original spectral bands: 6 No. of texture measures: 4 Spatial Distances of neighbors: 1 with window size of 5x5 2 with window size of 7x7 Directions of the neighbors: 4 (0, 45, 90 and 135) The outputs of the texture measures were then scaled to 0 to 255 to match the spectral data range. Together with original 6 spectral bands, there are 198 features available. The list of the features is given in Table 1. However, it is inappropriate to use all of them directly for classification. Obviously, not all the features are useful for individual projects. The useless features damage the reliability of class training. It often leads to high classification accuracy on training data but low on testing data, and therefore the rest of the image. In other words, the class model has poor generalization. Feature reduction is a must step before classification can be carried out. Table 1: The list of spectral and spatial features Feature Numbers Features 1 6 ETM+ Bands: B1 to B6 730 Contrast in 4 directions at distance 1 for B1 to B6 31 54 Correlation in 4 directions at distance 1 for B1 to B6 55 78 Energy in 4 directions at distance 1 for B1 to B6 79 102 Homogeneity in 4 directions at distance 1 for B1 to B6 103 198 Same as 7 102 but at distance 2 Feature Extraction and Selection It is expected some texture features are highly correlated. After examining the correlation properties among all the features shown in Fig. 1, it was found that the Homogeneity features for the 6 spectral bands in all the directions with the same spatial distance are highly correlated. The Energy features are insensitive to directions as well. Each of this highly correlated group of features has been compressed by principal component transform and replaced by the first component, respectively, leaving 116 features for further consideration. Further feature selection can be performed based on class separability provided by a subset of data. The separability is related to mean vector difference and/or covariance matrix difference. Since the wide variation of texture for most of ground cover types, the normal distribution based separability measure, Bhattacharyya distance (B-dis), is preferred. B-dis between class i and class j with mean vector m i and m j , covariance matrix Σi and Σ j , respectively, is defined as [1]: B−dis = 1 8 (m i − m j ) t Σ i + Σ j 2 ⎧ ⎨ ⎩ ⎫ ⎬ ⎭ −1