Next Article in Journal
Ice Concentration Retrieval from the Analysis of Microwaves: Evaluation of a New Methodology Optimized for the Copernicus Imaging Microwave Radiometer
Previous Article in Journal
Inter-Comparisons of Daily Sea Surface Temperatures and In-Situ Temperatures in the Coastal Regions
Open AccessLetter

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

Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1593; https://doi.org/10.3390/rs12101593
Received: 7 April 2020 / Revised: 13 May 2020 / Accepted: 14 May 2020 / Published: 17 May 2020
(This article belongs to the Section Remote Sensing Letter)
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. View Full-Text
Keywords: metric learning; semi-supervised classification; manifold regularization metric learning; semi-supervised classification; manifold regularization
Show Figures

Graphical abstract

MDPI and ACS Style

Liu, H.; Luo, R.; Shang, F.; Meng, X.; Gou, S.; Hou, B. Semi-Supervised Deep Metric Learning Networks for Classification of Polarimetric SAR Data. Remote Sens. 2020, 12, 1593.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop