Fully Polarimetric Synthetic Aperture Radar (SAR) Processing for Crop Type Identification

The target or polarimetric decomposition is widely used to process multi-polarization SAR imagery to establish a correspondence between physical characteristics of interested objects and observed scattering mechanisms. Polarimetric decomposition parameters are used as the basis for developing new classification methods for analyzing polarimetric SAR data. This study proposes to combine two polarimetric decomposition parameters (entropy (H) and angle (α)) derived from the Cloude and Pottier decomposition method and total scattered power (Span) in crop type identification. Support vector machine (SVM) classification algorithm was selected as a classifier to resolve limitations of classifications based on polarimetric decomposition parameters. The advantages of the proposed method are determined by comparing with other commonly used methods based on polarimetric features and the results produced from the coherency matrix, i.e., without target decomposition. Results show that the proposed method is about 10 percent better than other methods based on polarimetric features without Span, and it outperforms the result from the coherency matrix with about 4 percent improvement in the overall accuracy.