Next Article in Journal
Optimal Scheduling and Fair Service Policy for STDMA in Underwater Networks with Acoustic Communications
Next Article in Special Issue
An Automatic and Novel SAR Image Registration Algorithm: A Case Study of the Chinese GF-3 Satellite
Previous Article in Journal
An Advanced IoT-based System for Intelligent Energy Management in Buildings
Previous Article in Special Issue
An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(2), 611; doi:10.3390/s18020611

Gaofen-3 PolSAR Image Classification via XGBoost and Polarimetric Spatial Information

1,* , 1
School of Electronic Information, Wuhan University, Wuhan 430072, China
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
Author to whom correspondence should be addressed.
Received: 31 December 2017 / Revised: 13 February 2018 / Accepted: 14 February 2018 / Published: 17 February 2018
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)


The launch of the Chinese Gaofen-3 (GF-3) satellite will provide enough synthetic aperture radar (SAR) images with different imaging modes for land cover classification and other potential usages in the next few years. This paper aims to propose an efficient and practical classification framework for a GF-3 polarimetric SAR (PolSAR) image. The proposed classification framework consists of four simple parts including polarimetric feature extraction and stacking, the initial classification via XGBoost, superpixels generation by statistical region merging (SRM) based on Pauli RGB image, and a post-processing step to determine the label of a superpixel by modified majority voting. Fast initial classification via XGBoost and the incorporation of spatial information via a post-processing step through superpixel-based modified majority voting would potentially make the method efficient in practical use. Preliminary experimental results on real GF-3 PolSAR images and the AIRSAR Flevoland data set validate the efficacy and efficiency of the proposed classification framework. The results demonstrate that the quality of GF-3 PolSAR data is adequate enough for classification purpose. The results also show that the incorporation of spatial information is important for overall performance improvement. View Full-Text
Keywords: Gaofen-3 (GF-3); polarimetric synthetic aperture radar (PolSAR); image classification; XGBoost; spatial information Gaofen-3 (GF-3); polarimetric synthetic aperture radar (PolSAR); image classification; XGBoost; spatial information

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

Dong, H.; Xu, X.; Wang, L.; Pu, F. Gaofen-3 PolSAR Image Classification via XGBoost and Polarimetric Spatial Information. Sensors 2018, 18, 611.

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



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top