SUPERVISED MANIFOLD-LEARNING ALGORITHM FOR POLSAR FEATURE EXTRACTION AND LULC CLASSIFICATION

Abstract. In this paper, a supervised manifold-learning method is proposed for PolSAR feature extraction and classification. Based on the tensor algebra, the proposed method characterizes each pixel with a local neighbourhood centered at it, thereby combining the spatial and polarimetric information within the image. The inherent spatial information is beneficial to alleviate the influence of speckle noise and improve the stability of the extracted features. In addition, the label information of training samples is utilized in feature extraction, therefore the discriminability of different classes can be well preserved. The tensor discriminative locality alignment (TDLA) method is applied to find the multilinear transformation from the original feature space to the low-dimensional feature space. Based on the extracted features in the low-dimensional space, the SVM classifier is applied to achieve the final classification result. A real PolSAR data set acquired over San Francisco is adopted for performance evaluation. The experimental results show that the proposed method can not only improve the classification accuracy, but also alleviate the influence of speckle noise. In addition, the spatial details can be well preserved, demonstrating the superior performance of the proposed method.