Texture analysis of AIRSAR images for land cover classification

Remote sensing technology and the advance of science are very useful information in land cover classification study. In this study, data from an airborne radar system, AIRSAR (Airborne Synthetic Aperture Radar) containing C, P and L bands each with HH, VV and HV polarizations were used to identify land cover features in two study areas in Kedah. The main objective of this study was to investigate the performance of each band and polarization for land cover classification by applying supervised classifiers. Texture measure such as VI, VA, VL, U, homogeineity, contrast, mean, standard deviation, entropy, angular second moment, GLDV angular second moment, GLDV entropy, mean Euclidean distance, skewness, kurtosis, energy, lacunarity and semivariogram were applied. For each measure, signature separability between classes selected from training areas was determined using Bhattacharyya distance. The texture measures were then used for supervised classification using Maximum Likelihood Classifier (MLC) and K Nearest Neighbor (kNN) classifiers. Accuracy assessment for each measure was carried out using random ground samples.