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26 October 2013 Segmentation and classification of PolSAR data using spectral graph partitioning
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Proceedings Volume 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 89210E (2013) https://doi.org/10.1117/12.2031128
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China
Abstract
Polar metric synthetic aperture radar (PolSAR) image classification is an important technique in the remote sensing area, has been deeply studied for a couple of decades. This paper proposes a new approach for segmentation and classification of PolSAR datain two steps. First, segmentation is performed based on spectral graph partitioning using edge information. Graph partitioning process is completed using the normalized cut criterion. Then, classification is performed based on the object level. We use Cloude and Pottier‟s method to initially classify the PolSAR image. The initial classification map defines training sets for classification based on the Wishart distribution. The advantages of this method are the automated classification, and the interpretation of each class based on the region‟s scattering mechanism. We tested this object-based analysis on our study area. It showed that this result well overcome the pepper-sault phenomenon appearing in the one using traditional pixel-based method, providing robust performance and the results more understandable and easier for further analyses
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Lei Zhao and Erxue Chen "Segmentation and classification of PolSAR data using spectral graph partitioning", Proc. SPIE 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 89210E (26 October 2013); https://doi.org/10.1117/12.2031128
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