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Journal of Applied Remote Sensing

Multifeature fusion for polarimetric synthetic aperture radar image classification of sea ice
Author(s): Hao Guo; Qing Fan; Xi Zhang; Jubai An
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Paper Abstract

Sea ice conditions are so heterogeneous, and the differences between the different ice types are less varied than that of land targets, so only using polarimetric or textural features would lead to misclassification of polarimetric synthetic aperture radar (PolSAR) data of sea ice. To support the identification of different ice types, the fusion of textural and polarimetric features would be a good solution. Simple discrimination analysis is used to rationalize a preferred features subset. Some features are analyzed, which include entropy H/alphaα/anisotropyA and three kinds of texture statistics (entropy, contrast, and correlation), in the C- and L-band polarimetric mode. After that, a multiobjective fuzzy decision model is proposed for supervised PolSAR data classification of sea ice, and the targets are categorized according to the principle of maximum membership grade. In consideration of the interference of the correlation among features, the model is based on Mahalanobis distance in which the covariances between the selected heterogeneous features could restrain the interference among redundant features. In the end, the effectiveness of the algorithm for PolSAR image classification of sea ice is demonstrated through the analysis of some experimental results.

Paper Details

Date Published: 3 November 2014
PDF: 21 pages
J. Appl. Rem. Sens. 8(1) 083534 doi: 10.1117/1.JRS.8.083534
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Hao Guo, Dalian Maritime Univ. (China)
Qing Fan, Dalian Maritime Univ. (China)
Xi Zhang, State Oceanic Administration (China)
Jubai An, Dalian Maritime Univ. (China)

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