Next Article in Journal
Evaluation of a Simplified Method to Estimate the Peak Inter-Story Drift Ratio of Steel Frames with Hysteretic Dampers
Next Article in Special Issue
Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery
Previous Article in Journal
Improved Imaging of Magnetically Labeled Cells Using Rotational Magnetomotive Optical Coherence Tomography
Previous Article in Special Issue
A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Appl. Sci. 2017, 7(5), 447; doi:10.3390/app7050447

Dual-Branch Deep Convolution Neural Network for Polarimetric SAR Image Classification

1
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
2
Department of Computer and Electronic Engineering, Wuzhou University, Wuzhou 543000, China
3
Cognitive Signal-Image and Control Processing Research Laboratory, School of Natural Sciences University of Stirling, Stirling FK9 4LA, UK
4
Space Mechatronic System Technology Laboratory, Department of Design, Manufacture and Engineering Management University of Strathclyde, Glasgow G1 1XJ, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Carlos López-Martínez and Juan Manuel Lopez-Sanchez
Received: 9 March 2017 / Revised: 23 April 2017 / Accepted: 24 April 2017 / Published: 27 April 2017
(This article belongs to the Special Issue Polarimetric SAR Techniques and Applications)
View Full-Text   |   Download PDF [3647 KB, uploaded 27 April 2017]   |  

Abstract

The deep convolution neural network (CNN), which has prominent advantages in feature learning, can learn and extract features from data automatically. Existing polarimetric synthetic aperture radar (PolSAR) image classification methods based on the CNN only consider the polarization information of the image, instead of incorporating the image’s spatial information. In this paper, a novel method based on a dual-branch deep convolution neural network (Dual-CNN) is proposed to realize the classification of PolSAR images. The proposed method is built on two deep CNNs: one is used to extract the polarization features from the 6-channel real matrix (6Ch) which is derived from the complex coherency matrix. The other is utilized to extract the spatial features of a Pauli RGB (Red Green Blue) image. These extracted features are first combined into a fully connected layer sharing the polarization and spatial property. Then, the Softmax classifier is employed to classify these features. The experiments are conducted on the Airborne Synthetic Aperture Radar (AIRSAR) data of Flevoland and the results show that the classification accuracy on 14 types of land cover is up to 98.56%. Such results are promising in comparison with other state-of-the-art methods. View Full-Text
Keywords: polarimetric SAR images; deep convolution neural network; dual-branch convolution neural network; land cover classification polarimetric SAR images; deep convolution neural network; dual-branch convolution neural network; land cover classification
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Gao, F.; Huang, T.; Wang, J.; Sun, J.; Hussain, A.; Yang, E. Dual-Branch Deep Convolution Neural Network for Polarimetric SAR Image Classification. Appl. Sci. 2017, 7, 447.

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.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top