Next Article in Journal
MMASTER: Improved ASTER DEMs for Elevation Change Monitoring
Previous Article in Journal
Multi-Channel Deconvolution for Forward-Looking Phase Array Radar Imaging
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(7), 700; https://doi.org/10.3390/rs9070700

Mapping Typical Urban LULC from Landsat Imagery without Training Samples or Self-Defined Parameters

1
State Key Laboratory of Geological Process and Mineral Resources, Planetary Science Institute, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
2
Department of Geography, University of South Carolina, 709 Bull St., Columbia, SC 29208, USA
3
Three Gorges Research Center for Geo-Hazard, Ministry of Education, China University of Geosciences, Wuhan 430074, China
4
Institute of Geophysics & Geomatics, China University of Geosciences, Wuhan 430074, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Magaly Koch and Prasad S. Thenkabail
Received: 14 June 2017 / Revised: 26 June 2017 / Accepted: 28 June 2017 / Published: 7 July 2017
Full-Text   |   PDF [22656 KB, uploaded 7 July 2017]   |  

Abstract

Land use/land cover (LULC) change is one of the most important indicators in understanding the interactions between humans and the environment. Traditionally, when LULC maps are produced yearly, most existing remote-sensing methods have to collect ground reference data annually, as the classifiers have to be trained individually in each corresponding year. This study presented a novel strategy to map LULC classes without training samples or assigning parameters. First of all, several novel indices were carefully selected from the index pool, which were able to highlight certain LULC very well. Following this, a common unsupervised classifier was employed to extract the LULC from the associated index image without assigning thresholds. Finally, a supervised classification was implemented with samples automatically collected from the unsupervised classification outputs. Results illustrated that the proposed method could achieve satisfactory performance, reaching similar accuracies to traditional approaches. Findings of this study demonstrate that the proposed strategy is a simple and effective alternative to mapping urban LULC. With the proposed strategy, the budget and time required for remote-sensing data processing could be reduced dramatically. View Full-Text
Keywords: urbanization; remote-sensing indices; unsupervised classification; automated supervised classification urbanization; remote-sensing indices; unsupervised classification; automated supervised classification
Figures

Graphical abstract

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

Share & Cite This Article

MDPI and ACS Style

Li, H.; Wang, C.; Zhong, C.; Zhang, Z.; Liu, Q. Mapping Typical Urban LULC from Landsat Imagery without Training Samples or Self-Defined Parameters. Remote Sens. 2017, 9, 700.

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