Abstract

ABSTRACT Classification of Polarimetric SAR (PolSAR) imagery is still one of the challenges in active remote sensing applications. Although a large number of features and different classifiers have been proposed, no unique approach has been found to satisfy all of the images and classes yet. In this paper, given the extracted features from PolSAR data, a new class-based feature selection (CBFS) algorithm is proposed to find the most optimum features for each class. Maximizing the discrimination of each class from the others is the main contribution of the CBFS which yields distinctive features. The selected features are then employed by classifiers to generate different classification results. Finally, a new approach is developed to combine these classification results to produce the final land cover map. Five different classifiers of Wishart Maximum Likelihood, Gaussian Maximum Likelihood, Support Vector Machine, Multi Layer Perceptron and Fuzzy Inference System are also used for classification. Given the CBFS results, two different Radarsat-2 and AirSAR PolSAR data were classified. Selected features led to improvement of about 5% in producer accuracies in comparison with two well-known Genetic Algorithm Feature Selection (GAFS) and Prototype Space Feature Selection (PSFS) methods. Moreover, Comparison results demonstrate that the fuzzy classifiers could improve the accuracies about 3% if they are suitably constructed and well designed. The achieved higher overall accuracy for the final classified map shows the effectiveness of the proposed approach over the other compared classification procedures.

Highlights

  • Full Polarimetric Synthetic Aperture Radar (PolSAR) images have been successfully used in different applications such as forestry, agriculture, urban, land cover/land use classification and etc (Lee & Pottier, 2009)

  • Selected features led to improvement of about 5% in producer accuracies in comparison with two well-known Genetic Algorithm Feature Selection (GAFS) and Prototype Space Feature Selection (PSFS) methods

  • Since common procedures for Polarimetric SAR (PolSAR) data classification have been developed by making a variation in feature selection and classifier schemes, different feature selection algorithms and different classifiers were used to evaluate the proposed method

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Summary

Introduction

Full Polarimetric Synthetic Aperture Radar (PolSAR) images have been successfully used in different applications such as forestry, agriculture, urban, land cover/land use classification and etc (Lee & Pottier, 2009) Most of these applications require classified PolSAR data. Considering the backscattering values, Covariance and Coherency matrices as original features of PolSAR images, lots of other features have been developed and employed to classify PolSAR data in a supervised and unsupervised manner These features mainly include SAR discriminators, coherent and incoherent target decomposition parameters which most of them try to classify pixels into different physical backscattering mechanisms of single bounce, double bounce, volume and helix scattering. Detailed explanation about these parameters could be found in (Lee & Pottier, 2009)

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