A Statistical-Measure-Based Adaptive Land Cover Classification Algorithm by Efficient Utilization of Polarimetric SAR Observables

The polarimetric information contained in polarimetric synthetic aperture radar (SAR) images represents great potential for characterization of natural and urban surfaces. However, it is still challenging to identify different land cover classes with polarimetric data. Most of the classification algorithms presented earlier have used a fixed value of polarimetric indexes for segregation of a particular land cover type from other classes. However, the value of these polarimetric indexes may change accordingly with change in observation site, temporal acquisition, environmental conditions, and calibration differences among various systems. Thus, the value of polarimetric indexes for segregation of each land cover type has to be tuned in order to cope with these changes. Therefore, in this paper, a decision-tree-based adaptive land cover classification technique has been proposed for labeling of different clusters to their own classes. The proposed method uses spatial-statistics-based expressions (i.e., median “ M” and standard deviation “ S”) of best-selected polarimetric indexes on the basis of a separability index criterion for creating the decision boundary among various classes. In order to make the system adaptive in nature, unknown terms have been included in the expressions. Due to the dependence of a developed nonlinear relationship of overall classification accuracy (OA) on large number of unknowns, a genetic algorithm (GA) approach has been used, which provides optimum values of considered polarimetric indexes for automatic segregation of different classes. The proposed algorithm is successfully tested and validated on ALOS PALSAR quad-pol data.

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