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ISPRS Int. J. Geo-Inf. 2017, 6(4), 97; doi:10.3390/ijgi6040097

A Sparse Manifold Classification Method Based on a Multi-Dimensional Descriptive Primitive of Polarimetric SAR Image Time Series

1,2,* , 1
,
1
,
3,* and 2
1
Signal Processing Laboratory, Electronic Information School, Wuhan University, Wuhan 430072, China
2
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3
Remote Sensing and Information Engineering School, Wuhan University, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 18 January 2017 / Revised: 13 March 2017 / Accepted: 25 March 2017 / Published: 29 March 2017
View Full-Text   |   Download PDF [4222 KB, uploaded 29 March 2017]   |  

Abstract

Classification using the rich information provided by time-series and polarimetric Synthetic Aperture Radar (SAR) images has attracted much attention. The key point is to effectively reveal the correlation between different dimensions of information and form a joint feature. In this paper, a multi-dimensional SAR descriptive primitive for each single pixel is firstly constructed, which in the polarimetric scale obtains incoherent information through target decompositions while in the time scale obtains coherent information through stochastic walk. Secondly, for the purpose of feature extraction and dimension reduction, a special feature space mapping for the descriptive primitive of the whole image is proposed based on sparse manifold expression and compressed sensing. Finally, the above feature is inputted into a support vector machine (SVM) classifier. This proposed method can inherently integrate the features of polarimetric SAR times series. Experiment results on three real time-series polarimetric SAR data sets show the effectiveness of our presented approach. The idea of a multi-dimensional descriptive primitive as a convenient tool also opens a new spectrum of potential for further processing of polarimetric SAR image time series. View Full-Text
Keywords: polarimetric SAR time series; image classification; multi-dimensional descriptive primitive; sparse manifold expression; compressed sensing polarimetric SAR time series; image classification; multi-dimensional descriptive primitive; sparse manifold expression; compressed sensing
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He, C.; Han, G.; Feng, D.; Du, J.; Liao, M. A Sparse Manifold Classification Method Based on a Multi-Dimensional Descriptive Primitive of Polarimetric SAR Image Time Series. ISPRS Int. J. Geo-Inf. 2017, 6, 97.

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