Eigen Value and Eigen Vector Based Decomposition and Wishart Supervised Classification on Fully Polarimetric SAR Data
Objectives: To study the decomposition parameters, target scattering mechanism using Eigen value Eigen vector based decomposition and classifying the filtered datasets with wishart supervised classifier, H a classification plane on fully Polarimetric Synthetic Aperture Radar (PolSAR) data. Methods/Analysis: Fully PolSAR datasets of Single Look Complex (SLC) product is selected for multiprocessing. As the SLC product contains Speckle, it has to be reduced using speckle filtering techniques, because speckle can degrade the accuracy of image classification. H/A/a decomposition theorem is applied on the dataset for classifying the scattering mechanism. Based on the H a plane, Polarimteric parameters further classifies the type of target. Wishart supervised classification and H a classification are performed on the filtered dataset to classify the image. Full polaraimetric Radarsat 2 datasets are used in this study. Findings: The entropy is a fundamental key parameter in determining the randomness of the model thus showing the importance of polarimetry in solving remote sensing problems. Supervised Wishart Classification accuracy assessment was made using Confusion matrix. The results of the outcomes are satisfactory. Novelty/Improvement: Classifying SAR data with H/A/a decomposition theorem using decomposition parameters and calculating accuracy assessment using Confusion matrix. Analysing, estimating the physical parameters using eigenvalues li, entropy H, alpha angle a and anisotropy A.