Segmentation and classification of vegetated areas using polarimetric SAR image data

Classification of radar images based on the information provided by individual pixels cannot generally produce satisfactory results due to speckle. The classification based on area analysis is therefore expected to be more accurate, as a uniform area, which usually consists of multipixels, provides reliable measurement statistics and texture characteristics. However, the area analysis requires partitions of uniform areas to be performed first. In this paper, an approach to the classification of radar images is developed based on two steps. First an image is partitioned into uniform areas (segments), and then these segments are classified. Both segmentation and classification are achieved by using the Gaussian Markov random field model. Test images are classified to demonstrate the method.