Polarimetric SAR Image Classification Using Radial Basis Function Neural Network

Abstract

Abstract-This paper presents a robust radial basis function (RBF) network based classifier for polarimetric synthetic aperture radar (SAR) images. The proposed feature extraction process utilizes the covariance matrix, the gray level co-occurrence matrix (GLCM) based texture features, and the backscattering power (Span) combined with the H/α/A decomposition, which are projected onto a lower dimensional feature space using principal component analysis. For the classifier training two popular techniques are explored: conventional backpropagation (BP) and particle swarm optimization (PSO). By using both polarimetric covariance matrix and decomposition based pixel values and textural information (contrast, correlation, energy, and homogeneity) in the feature set, classification accuracy is improved. An experimental study is performed using the fully polarimetric San Francisco Bay and Flevoland data sets acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (AIRSAR) at L-band to evaluate the performance of the proposed classifier. Classification results (in terms of confusion matrix, overall accuracy and classification map) compared with competing state of the art algorithms demonstrate the effectiveness of the proposed RBF network classifier.

Structure Label Matrix Completion for PolSAR Image Classification

“…Next, various statistical distribution-based ML classification methods became widespread [11,19,27]. Meanwhile, machine-learning methods such as neural networks (NNs) [28], support vector machines (SVMs) [29], random forests [30], manifold-learning-based supervised graph embedding (SGE) algorithms [31], and convolutional neural networks (CNNs) [32] were proposed to make classification results robust and accurate. Unsupervised cluster methods first use Cloude-Pottier decomposition to create eight feature subspaces following the Wishart classifier [33].…”

Dynamic and data-driven classification for polarimetric SAR images

“…Currently, PolSAR image classification is mainly performed on a single SAR image with a static classifier, such as Multi-Layer Perceptron (MLP) 13 , MLP and logistic regression 1 , Random Forests 4 , Wishart-Classifier 28 , knowledge-based classifier 3 , Radial Basis Functions (RBFs) 8 , and SVMs 29 .…”