Next Article in Journal
Iridescent Perfect Absorption in Critically-Coupled Acoustic Metamaterials Using the Transfer Matrix Method
Next Article in Special Issue
Synergetic of PALSAR-2 and Sentinel-1A SAR Polarimetry for Retrieving Aboveground Biomass in Dipterocarp Forest of Malaysia
Previous Article in Journal
Terawatt-Isolated Attosecond X-ray Pulse Using a Tapered X-ray Free Electron Laser
Previous Article in Special Issue
A Multi-Year Study on Rice Morphological Parameter Estimation with X-Band Polsar Data
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Appl. Sci. 2017, 7(6), 612; doi:10.3390/app7060612

Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information

1
Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China
2
Huiding Technology Co. Ltd., Chengdu 611731, China
*
Author to whom correspondence should be addressed.
Academic Editors: Carlos López-Martínez and Juan M. Lopez-Sanchez
Received: 14 April 2017 / Revised: 31 May 2017 / Accepted: 9 June 2017 / Published: 13 June 2017
(This article belongs to the Special Issue Polarimetric SAR Techniques and Applications)
View Full-Text   |   Download PDF [2857 KB, uploaded 15 June 2017]   |  

Abstract

The composite kernel feature fusion proposed in this paper attempts to solve the problem of classifying polarimetric synthetic aperture radar (PolSAR) images. Here, PolSAR images take into account both polarimetric and spatial information. Various polarimetric signatures are collected to form the polarimetric feature space, and the morphological profile (MP) is used for capturing spatial information and constructing the spatial feature space. The main idea is that the composite kernel method encodes diverse information within a new kernel matrix and tunes the contribution of different types of features. A support vector machine (SVM) is used as the classifier for PolSAR images. The proposed approach is tested on a Flevoland PolSAR data set and a San Francisco Bay data set, which are in fine quad-pol mode. For the Flevoland PolSAR data set, the overall accuracy and kappa coefficient of the proposed method, compared with the traditional method, increased from 95.7% to 96.1% and from 0.920 to 0.942, respectively. For the San Francisco Bay data set, the overall accuracy and kappa coefficient of the proposed method increased from 92.6% to 94.4% and from 0.879 to 0.909, respectively. Experimental results verify the benefits of using both polarimetric and spatial information via composite kernel feature fusion for the classification of PolSAR images. View Full-Text
Keywords: PolSAR; image classification; composite kernel; polarimetric features; spatial features; feature fusion PolSAR; image classification; composite kernel; polarimetric features; spatial features; feature fusion
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

Wang, X.; Cao, Z.; Ding, Y.; Feng, J. Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information. Appl. Sci. 2017, 7, 612.

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