A full understanding of radar backscatter from urban areas is necessary in order to develop a robust methodology for monitoring and classifying urban characteristics using remotely sensed Synthetic Aperture Radar images. This paper examines the dominant backscattering mechanisms such as single bounce from roofs, double bounce from wall± ground structures and possibly triple bounce from wall± wall± ground structures, and their relative contributions to the backscatter. With the use of quad-polarized image data such as those acquired by the NASA/JPL AirSAR system, the backscatter can be decomposed into components caused by di erent backscattering mechanisms, o ering a promise for urban monitoring and classi® cation.
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.Index Terms-Classification, Gaussian random Markov field (GRMF) model, synthetic aperture radar (SAR) image, segmentation.
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