A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations
Citation
Haifa Tamiminia a, Saeid Homayouni b, Heather McNairn c, Abdoreza Safari a
Plain language summary
L-band airborne Synthetic Aperture Radar (SAR) data were used in this study to assess a new classification algorithm to detect crop types in Manitoba, Canada. Several polarimetric SAR features were generated from the radar imagery. These features were used in an optimized kernel-based C-means clustering algorithm for the purpose of separating and identifying the type of crop. This new classifier provided maps that were 12-17% more accurate when compared to conventional classification algorithms.
Abstract
Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific characteristics such as high resolution, weather and daylight independence, have become a valuable source of information for environment monitoring and management. The discrimination capability of observations acquired by these sensors can be used for land cover classification and mapping. The aim of this paper is to propose an optimized kernel-based C-means clustering algorithm for agriculture crop mapping from multi-temporal PolSAR data. Firstly, several polarimetric features are extracted from preprocessed data. These features are linear polarization intensities, and several statistical and physical based decompositions such as Cloude-Pottier, Freeman-Durden and Yamaguchi techniques. Then, the kernelized version of hard and fuzzy C-means clustering algorithms are applied to these polarimetric features in order to identify crop types. The kernel function, unlike the conventional partitioning clustering algorithms, simplifies the non-spherical and non-linearly patterns of data structure, to be clustered easily. In addition, in order to enhance the results, Particle Swarm Optimization (PSO) algorithm is used to tune the kernel parameters, cluster centers and to optimize features selection. The efficiency of this method was evaluated by using multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Manitoba, Canada, during June and July in 2012. The results demonstrate more accurate crop maps using the proposed method when compared to the classical approaches, (e.g. 12% improvement in general). In addition, when the optimization technique is used, greater improvement is observed in crop classification, e.g. 5% in overall. Furthermore, a strong relationship between Freeman-Durden volume scattering component, which is related to canopy structure, and phenological growth stages is observed.