Textural processing of multi-polarization SAR for agricultural crop classification

Three techniques for generating texture statistics are examined: the gray-level co-occurrence matrix (GLCM), the gray-level difference vector (GLDV) and the neighboring gray-level dependence matrix (NGLDM). The objective of these statistical approaches is to translate visual texture properties into quantitative descriptors in a manner that they can be used to discriminate relevant land features using additional image processing techniques. These second-order statistical methods are used to generate texture features from C-HH and C-HV airborne synthetic aperture radar (SAR) data collected on July 10, 1990 over an agricultural area in southern Ontario Canada. Texture features generated from the GLCM, GLDV and NGLDM are classified individually using a k-nearest neighbor (k-NN) supervised classifier. The greatest classification improvement (/spl ap/20%) was observed with the mean and correlation texture features derived from the GLCM. However, the selection of a specific second-order statistical technique may not be critical, since similar classification improvements were observed for the GLCM, GLDV and NGLDM statistical techniques. The results reported here highlight the importance of texture processing to methods of classifying agricultural crops using SAR data.