Improving cover type identification in speckled SAR images by prefiltering and sequential classification

Synthetic aperture radar utilizes coherent microwaves to produce images of the earth's surface. Due to the interference of coherent wavelets, the images appear speckled. This reduces the performance of per-pel classifiers. One way to increase the performance is to filter the image first, then classify the filtered image. For this purpose, several novel filters that have been reported in the literature are investigated. These are the geometric filter, adaptive LMMSE filter, and linear approximation filter. For comparison, conventional mean and median filters are also considered. It is found that the mean filter with seven iterations gives the best result. The overall performance increased from 65.2 to 88.9 percent. The capability of these filters to preserve edges in the original image are also assessed. It is seen that the geometric and median filters are the best in preserving edges, and that the linear approximation and adaptive LMMSE filters are the best in discriminating roads. Prefiltering the image effectively provides contextual information to the per-pel classifier. An alternate approach is to directly design a contextual classifier. A new contextual classifier based on sequential decision theory is proposed. With this classifier, it is found that the overall performance increases to 89.5 percent.