Next Article in Journal
Joint Local Abundance Sparse Unmixing for Hyperspectral Images
Previous Article in Journal
Tensor-Based Sparse Representation Classification for Urban Airborne LiDAR Points
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(12), 1222; doi:10.3390/rs9121222

Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

1
Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
2
College of Information Engineering, Shenzhen University, Shenzhen 518060, China
3
College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China
4
College of Tourism and Geographical Sciences, Yunnan Normal University, Kunming 650500, China
*
Authors to whom correspondence should be addressed.
Received: 6 November 2017 / Revised: 22 November 2017 / Accepted: 25 November 2017 / Published: 27 November 2017
View Full-Text   |   Download PDF [10270 KB, uploaded 27 November 2017]   |  

Abstract

Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence. The abundance and minimum noise fraction (MNF) results that were obtained from mixed pixel decomposition were added to decision tree multi-features using a three-dimensional (3D) Terrain model, which was created using an image fusion digital elevation model (DEM), to select training samples (ROIs), and improve ROI separability. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the Kappa coefficient and the overall accuracy of integrated pixel unmixing and decision tree method increased by 0.093% and 10%, respectively, as compared with the original decision tree method. This proposed method could effectively eliminate the influence of mixed pixels and improve the accuracy in complex LULC classifications. View Full-Text
Keywords: LULC; improved decision tree; mixed pixel decomposition; 3D terrain; fusion LULC; improved decision tree; mixed pixel decomposition; 3D terrain; fusion
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).

Share & Cite This Article

MDPI and ACS Style

Yang, C.; Wu, G.; Ding, K.; Shi, T.; Li, Q.; Wang, J. Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods. Remote Sens. 2017, 9, 1222.

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