Next Article in Journal
Towards Detection of Cutting in Hay Meadows by Using of NDVI and EVI Time Series
Previous Article in Journal
Soil Clay Content Mapping Using a Time Series of Landsat TM Data in Semi-Arid Lands
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(5), 6079-6106; doi:10.3390/rs70506079

Segmentation-Based PolSAR Image Classification Using Visual Features: RHLBP and Color Features

School of Electronic Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 19 November 2014 / Revised: 21 April 2015 / Accepted: 4 May 2015 / Published: 15 May 2015

Abstract

A segmentation-based fully-polarimetric synthetic aperture radar (PolSAR) image classification method that incorporates texture features and color features is designed and implemented. This method is based on the framework that conjunctively uses statistical region merging (SRM) for segmentation and support vector machine (SVM) for classification. In the segmentation step, we propose an improved local binary pattern (LBP) operator named the regional homogeneity local binary pattern (RHLBP) to guarantee the regional homogeneity in PolSAR images. In the classification step, the color features extracted from false color images are applied to improve the classification accuracy. The RHLBP operator and color features can provide discriminative information to separate those pixels and regions with similar polarimetric features, which are from different classes. Extensive experimental comparison results with conventional methods on L-band PolSAR data demonstrate the effectiveness of our proposed method for PolSAR image classification. View Full-Text
Keywords: polarimetric SAR; classification; SRM; RHLBP; color features polarimetric SAR; classification; SRM; RHLBP; color features
Figures

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

Cheng, J.; Ji, Y.; Liu, H. Segmentation-Based PolSAR Image Classification Using Visual Features: RHLBP and Color Features. Remote Sens. 2015, 7, 6079-6106.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top