Semi-Supervised Deep Metric Learning Networks for Classification of Polarimetric SAR Data

• Main Authors: Hongying Liu, Ruyi Luo, Fanhua Shang, Xuechun Meng, Shuiping Gou, Biao Hou
• Format: Article
• Language: English
• Published: MDPI AG 2020-05-01
• Series: Remote Sensing
• Subjects: metric learning; semi-supervised classification; manifold regularization
• Online Access: https://www.mdpi.com/2072-4292/12/10/1593

Recently, classification methods based on deep learning have attained sound results for the classification of Polarimetric synthetic aperture radar (PolSAR) data. However, they generally require a great deal of labeled data to train their models, which limits their potential real-world applications. This paper proposes a novel semi-supervised deep metric learning network (SSDMLN) for feature learning and classification of PolSAR data. Inspired by distance metric learning, we construct a network, which transforms the linear mapping of metric learning into the non-linear projection in the layer-by-layer learning. With the prior knowledge of the sample categories, the network also learns a distance metric under which all pairs of similarly labeled samples are closer and dissimilar samples have larger relative distances. Moreover, we introduce a new manifold regularization to reduce the distance between neighboring samples since they are more likely to be homogeneous. The categorizing is achieved by using a simple classifier. Several experiments on both synthetic and real-world PolSAR data from different sensors are conducted and they demonstrate the effectiveness of SSDMLN with limited labeled samples, and SSDMLN is superior to state-of-the-art methods.