A Spectral-textural Classifier For Digital Imagery

Spectral classification is commonly used in remote sensing as a means of extracting information from an image. Unfortunately, the desired classes cannot always be defined by their spectral properties. To overcome this problem, many texture classification methods have been developed. Nonetheless, no single method is widely accepted in remote sensing: some methods do not work with multiband data, some cannot extract irregularly-shaped, textured areas, some do not consider spectral properties, and some are too specific, resulting in scene dependency. In this study, a new classification method is developed, the spectral-texture classifier. The spectral-texture classifier works with multiband images, uses both spectral and spatial characteristics of the class, performs area-based rather than pixel-based classifications, and classifies irregularly-shaped areas. Landsat's Thematic Mapper data were used to test the spectral-texture classifier against the maximum likelihood classifier. It was found that the overall accuracy of classifying land cover with the spectral-texture classifier was higher than that with the maximum likelihood method. Moreover, since the spectral-texture classifier is an area-based classifier, its results do not exhibit the unrealistic isolated spots which degrade the results from single-pixel classifiers such as the maximum likelihood. Since the spectral-texture classifier considers both the spectral and the spatial characteristics of neighboring pixels simultaneously, the classification of textured areas, including the irregularly shaped textured areas, is accurate.