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.
Highlights
Synthetic aperture radar (SAR) can be used in a vast majority of application areas due to its ability to work day and night under all weather conditions
Considering the inconsistency between polarimetric incoherent scale and time-series coherent scale, a nonlinear classification model is further constructed based on sparse manifold expression and compressed sensing for feature extraction and dimension reduction
We propose a sparse manifold classification method with a multi-dimensional feature on PolSAR image time series
Summary
Synthetic aperture radar (SAR) can be used in a vast majority of application areas due to its ability to work day and night under all weather conditions. Representative instances are Branch-Cut Algorithm, Minimum Discontinuity, Mask-Cut Algorithm and Minimum Lp-Norm Phase Unwrapping [5] With this incoherent and coherent information, researchers have developed various image processing patterns for SAR classification from the combination of polarimetric distribution and classifiers [6] to the combination of extracted features and classifiers [7] and nowadays to deep learning [8]. Considering the inconsistency between polarimetric incoherent scale and time-series coherent scale, a nonlinear classification model is further constructed based on sparse manifold expression and compressed sensing for feature extraction and dimension reduction This model can deal with the inconsistency of the two scales and tactfully avoid the nonlinearity problem brought by the multiplicative model of SAR.
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