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The aim of this research is to evaluate crop discrimination using airborne radar data based on multipolarization and textural information. Multipolarization data (C-HH, C-VV, and C-HV) were used for discriminating 5 crop types i.e., corn, wheat, soya, pasture, and alfalfa. For the multipolarization evaluation, an unsupervised classification algorithm and a supervised method based on maximum likelihood were used on the data. For the textural evaluation, textural measures of different degrees were calculated on three different order histograms and were evaluated from the crop discrimination point of view. Results show that multipolarization correct classification rates of 86.31% and 74.47% were obtained for supervised and unsupervised methods respectively. Hence, multipolarization radar data offer an adequate tool for crop identification especially with supervised classification. The evaluation of textural measures shows that for a first order histogram the mean measure gives the best rate of discrimination. In the case of second and third order histograms, the best measures are contrast and large number emphasis respectively. These textural measures were integrated with the three multipolarization channels in order to determine their specific contributions. Results show that crop class separability is thereby improved and that the rate of correct classification increased by 9.79% for the crops.>
Anys et al. (Sun,) studied this question.