Metric Learning Based Fine-Grained Classification for PolSAR Imagery

Abstract

Polarimetric Synthetic Aperture Radar (PolSAR) image classification is an essential part of SAR data applications. As one of the image classification methods that can efficiently capture structural information and semantic context, the convolutional neural network (CNN) seems to be a solution for the classification issue in that it outperforms the classical supervised classifiers under the condition of sufficient training data, and it has been used in PolSAR classification widely. Simultaneously, the distance metric learning (DML) is proposed to improve the classification algorithms in performance and even in feature extraction. In this paper, DML with adaptive density discrimination regarded as a loss function, namely Magnet Loss, is applied to the classification of PolSAR images, and k-means++ is realized the clustering process for each category of training samples. Then, different classifiers are executed to replace the softmax function to achieve more accurate classification. Finally, a series of experiments are implemented to prove the effectiveness of the proposed method. Simultaneously, the samples of the coarse label are given and used to analyze the fine-grained classification algorithm by clustering.