论文信息 - Classification of multi-look polarimetric SAR imagery and polarization channel optimization

Classification of multi-look polarimetric SAR imagery and polarization channel optimization

In this paper, a new maximum likelihood (ML) classification algorithm is proposed to classify the multi-look polarimetric synthetic aperture radar (SAR) imagery. Experimental results with the NASA/JPL airborne L-band polarimetric SAR data demonstrate the effectiveness of the new algorithm. Furthermore, when using the algorithm in the classifications with subsets of the multi-look polarimetric SAR data, the polarization-channel optimization for the terrain type classification is implemented.

Hong Xiong | Guoqing Liu | Shunji Huang | F. Rubertone | A. Torre

[1] Victor S. Frost,et al. Maximum likelihood classification of synthetic aperture radar imagery , 1985, Comput. Vis. Graph. Image Process..

[2] F. Ulaby,et al. Radar polarimetry for geoscience applications , 1990 .

[3] G. De Grandi,et al. Supervised Fully Polarimetric Classification: An Experimental Study On The Maestro1 Freiburg Data Set , 1992, [Proceedings] IGARSS '92 International Geoscience and Remote Sensing Symposium.

[4] Jin Au Kong,et al. Classification of Earth Terrain Using Polarimetric Synthetic Aperture Radar Images , 1989, Progress In Electromagnetics Research.

[5] Ron Kwok,et al. Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution , 1994 .

[6] Jan P. Allebach,et al. Combating Speckle In SAR Images: Vector Filtering And Sequential Classification Based On A Multiplicative Noise Model , 1990 .