Quantitative Textural Parameter Selection for Residential Extraction from High-resolution Remotely Sensed Imagery

Residential areas show plenty of texture information on high resolution remotely sensed imagery. Appropriate description about this texture information for discriminating residential class and its background is a key problem for improving the classification results. Method for selecting proper texture parameters is presented in this paper. Based on the analysis of residential texture, grey level cooccurrence matrix (GLCM) and edge density (ED) approaches with candidate nine texture measurements (contrast, homogeneity, dissimilarity entropy, energy, mean, standard deviation, correlation and edge density) is selected as candidate texture measurements. The texture parameters are selected based on separability measured by Jeffries-Matusita distance (JM distance) between residential and its background in corresponding texture space. IKONOS panchromatic imagery has been used as example and the optimal texture parameters were selected by using the proposed method.