LAUSR

Entropy score is defined to be a metric indicating the class separation, which is then used in feature selection process to improve the moment-based classification performance of synthetic aperture radar (SAR) images. Feature extraction is performed over each SAR image by employing different moment methods to enrich the feature space before feature selection. Fusing all the features coming from different moment methods into a single vector is not feasible since the vector will have high dimensionality and embedded redundancies due to correlations among features. To reduce the dimensionality of feature space and increase the discrimination capability of the feature vector, a unique approach based on entropy score selecting top k methods for each feature coefficient is proposed. Experimental results verify the improvement of the accuracy on SAR image classification over the state-of-the-art.