Crop discrimination based on polarimetric correlation coefficients optimization for PolSAR data

Abstract

Crop discrimination is a necessary step for most agricultural monitoring systems. Radar polarimetric responses from various crops strongly relate to the types and orientations of the local scatterers, which makes the discrimination still difficult using the polarimetric synthetic aperture radar (PolSAR) technique. This work provides a new approach by investigating and utilizing the characteristics of polarimetric correlation coefficients in the rotation domain along the radar line of sight. The theoretical basis lies in that polarimetric correlation coefficients can reflect the different responses and can be enhanced at different levels for various land-cover types with suitable rotation angles in the rotation domain. In this vein, a polarimetric correlation coefficient optimization framework is established and new polarimetric features are extracted therein. Demonstration with multi-frequency (P-, L-, and C-bands) airborne synthetic aperture radar (AIRSAR) PolSAR data over crop areas validates that polarimetric correlation coefficients are crop dependent and the optimized polarimetric correlation coefficient parameters can better discriminate them. Then, a crop discrimination scheme is proposed using the derived polarimetric features. A flow chart for the optimal discrimination feature set selection and determination is provided and is validated by the real data with seven typical crop types. All these crop types are successfully discriminated for the P- and L-band data, whereas only two types of crops are slightly overlapped in the feature space for the C-band data. Experimental studies demonstrate the efficiency and potential of the established methodology.

Acknowledgement

The authors would like to thank Dr T. L. Ainsworth and Dr J. S. Lee of Naval Research Laboratory, Washington, USA, for providing the ground truth of AIRSAR data.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grant number 41301490], [grant number 61490690], [grant number 61490692].