Urban built-up area extraction using combined spectral information and multivariate texture

Urban built-up area information is required by many applications, such as research of urbanization rate. Urban built-up area extraction using moderate resolution remotely sensed data (e.g. Landsat TM/ETM+) presents numerous challenges, such as very heterogeneous spectral features of urban areas, spectral confusion between built-up class and others. Considering that image texture is one of the important spatial information for identifying urban land cover, a new methodology to address these issues is proposed. This approach involves processes as the following, as a first step, multivariate texture is computed through multivariate variogram. Spectral bands and multivariate texture are then combined in classification process for built-up area extraction. One-Class Support Vector Machine (OCSVM) classifier was used in this process. A comprehensive evaluation is present with Landsat TM data of Beijing, China. Results demonstrate that the proposed method significantly improves the accuracy of urban area extraction.