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

Hongying Liu,Fanhua Shang,Shuiping Gou,Xuechun Meng,Ruyi Luo,Biao Hou

doi:10.3390/rs12101593

Abstract

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.

Highlights

As the synthetic aperture radar (SAR) sensors can work independently in various weather conditions, they have been widely applied to disaster detection and military reconnaissance
The Flevoland data set: This data set is acquired by the NASA/JPL AIRSAR system, and it is publicly available from the European Space Agency (ESA)
The metric learning is utilized to construct a layer-wise network, which transforms the linear mapping to the non-linear projection for learning the intuitive features from Polarimetric SAR (PolSAR) data

Summary

Introduction

As the synthetic aperture radar (SAR) sensors can work independently in various weather conditions, they have been widely applied to disaster detection and military reconnaissance. Researchers have proposed numerous classification algorithms during the last few years. These methods broadly fall into the following three categories: supervised, unsupervised, and semi-supervised methods. The unsupervised classification methods infer the label of each sample from the input dataset without pre-existing labels These methods play a main role in the early years (e.g., the late 20th century) for PolSAR data. The classification results benefit from the direct analysis on the physical scattering mechanisms and the statistical characteristics of terrain types. These unsupervised methods rarely yield high classification accuracies due to the lack of prior knowledge of the terrain classes

Methods

Results

Conclusion