THE LOW BACKSCATTERING TARGETS CLASSIFICATION IN URBAN AREAS

• Main Author: L. Shi
• Format: Article
• Language: English
• Published: Copernicus Publications 2012-07-01
• Series: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-7/171/2012/isprsannals-I-7-171-2012.pdf

The Polarimetric and Interferometric Synthetic Aperture Radar (POLINSAR) is widely used in urban area nowadays. Because of the physical and geometric sensitivity, the POLINSAR is suitable for the city classification, power-lines detection, building extraction, etc. As the new X-band POLINSAR radar, the china prototype airborne system, XSAR works with high spatial resolution in azimuth (0.1 m) and slant range (0.4 m). In land applications, SAR image classification is a useful tool to distinguish the interesting area and obtain the target information. The bare soil, the cement road, the water and the building shadow are common scenes in the urban area. As it always exists low backscattering sign objects (LBO) with the similar scattering mechanism (all odd bounce except for shadow) in the XSAR images, classes are usually confused in Wishart-H-Alpha and Freeman-Durden methods. It is very hard to distinguish those targets only using the general information. To overcome the shortage, this paper explores an improved algorithm for LBO refined classification based on the Pre-Classification in urban areas. Firstly, the Pre-Classification is applied in the polarimetric datum and the mixture class is marked which contains LBO. Then, the polarimetric covariance matrix C3 is re-estimated on the Pre-Classification results to get more reliable results. Finally, the occurrence space which combining the entropy and the phase-diff standard deviation between HH and VV channel is used to refine the Pre-Classification results. The XSAR airborne experiments show the improved method is potential to distinguish the mixture classes in the low backscattering objects.