Multivariate Image Texture by Multivariate Variogram for Multispectral Image Classification

Traditional image texture measure usually allows a texture description of a single band of the spectrum, characterizing the spatial variability of gray-level values within the singleband image. A problem with the approach while applied to multispectral images is that it only uses the texture information from selected bands. In this paper, we propose a new multivariate texture measure based on the multivariate variogram. The multivariate texture is computed from all bands of a multispectral image, which characterizes the multivariate spatial autocorrelation among those bands. In order to evaluate the performance of the proposed texture measure, the derived multivariate texture image is combined with the spectral data in image classification. The result is compared to classifications using spectral data alone and plus traditional texture images. A machine learning classifier based on Support Vector Machines (SVMs) is used for image classification. The experimental results demonstrate that the inclusion of multivariate texture information in multispectral image classification significantly improves the overall accuracy, with 5 to 13.5 percent of improvement, compared to the classification with spectral information alone. The results also show that when incorporated in image classification as an additional band, the multivariate texture results in high overall accuracy, which is comparable with or higher than the best results from the existing single-band and two-band texture measures, such as the variogram, cross variogram and Gray-Level Co-occurrence Matrix (GLCM) based texture. Overall, the multivariate texture provides the useful spatial information for land-cover classification, which is different from the traditional single band texture. Moreover, it avoids the band selection procedure which is prerequisite to traditional texture computation and would help to achieve high accuracy in the most classification tasks. Introduction It is often found that classes of land-cover may be discriminated from multispectral imagery on the basis of their spectral signature, but also of their texture (Jensen, 1982; Franklin and Peddle, 1990; Gong et al., 1992; Franklin et al., 2001; Coburn and Roberts, 2004). Image texture, as one of the important Multivariate Image Texture by Multivariate Variogram for Multispectral Image Classification Peijun Li, Tao Cheng, and Jiancong Guo spatial information types from the image, has been widely used in remote sensing image classification, image segmentation, and other fields of information processing, which significantly improves the overall accuracy in most cases (e.g., Jensen, 1982; Cohen, 1990; Franklin and Peddle, 1990; Gong et al., 1992; Ryherd and Woodcock, 1996; Franklin et al., 2001; Coburn and Roberts, 2004). The traditional image texture measure, such as the classical Gray-Level Co-occurrence Matrix (GLCM) method (Haralick et al., 1973), usually allows a texture description of a single spectral band, which only characterizes the spatial variability (or spatial autocorrelation) of the spectral feature (e.g., Digital Number) within the band. Thus, texture is usually extracted individually from a single band. However, texture features from the different bands of a multispectral image are generally different and have different discriminating capability of land-cover types. Thus, while the image texture is used in the multispectral image classification, an individual band usually has to be first decided for texture computation in order to obtain a high accuracy (Marceau et al., 1990; Arbarca-Hernandez and ChicaOlmo, 1999; Berberoglu et al., 2000; Chica-Olmo and ArbarcaHernandez, 2000; Coburn and Roberts, 2004). This could be accomplished either by selecting one directly from the original multispectral bands (Marceau et al., 1990; Coburn and Roberts, 2004), or by first conducting the Principal Component Analysis (PCA) on the original multispectral image and then selecting one or two PCs (e.g., Arbarca-Hernandez and Chica-Olmo, 1999; Berberoglu et al., 2000; Chica-Olmo and Arbarca-Hernandez, 2000). However, a problem with the approach is that it only uses the texture information from the selected bands or components, not accounting for the spatial autocorrelation among the bands. Recently, some two-band (or between-band) texture measures were proposed to express the between-band spatial correlation, such as cross variogram, pseudo cross variogram (Chica-Olmo and Arbarca-Hernandez, 2000), and opponent color texture (Jain and Healey, 1998). The between-band texture was found to provide very useful spatial information for discrimination between different land-cover types (ChicaOlmo and Arbarca-Hernandez, 2000). Most optical remote sensing images consist of multiple bands (e.g., multispectral image), which record the information from different parts of the electromagnetic spectrum. PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING Feb r ua r y 2009 147 Peijun Li and Jiancong Guo are with the Institute of Remote Sensing and GIS, Peking University, Beijing 100871, P R China ([email protected]). Tao Cheng is with the Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2E3. Photogrammetric Engineering & Remote Sensing Vol. 75, No. 2, February 2009, pp. 147–157. 0099-1112/09/7502–0147/$3.00/0 © 2009 American Society for Photogrammetry and Remote Sensing 147-157_06-124.qxd 1/13/09 4:10 PM Page 147