Next Article in Journal
Crop Monitoring Based on SPOT-5 Take-5 and Sentinel-1A Data for the Estimation of Crop Water Requirements
Next Article in Special Issue
Application of Helmert Variance Component Based Adaptive Kalman Filter in Multi-GNSS PPP/INS Tightly Coupled Integration
Previous Article in Journal
Erratum: Dupuy, E.; et al. Comparison of XH2O Retrieved from GOSAT Short-Wavelength Infrared Spectra with Observations from the TCCON Network. Remote Sensing 2016, 8, 414
Previous Article in Special Issue
Merging Alternate Remotely-Sensed Soil Moisture Retrievals Using a Non-Static Model Combination Approach
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(6), 521; doi:10.3390/rs8060521

Generation of Land Cover Maps through the Fusion of Aerial Images and Airborne LiDAR Data in Urban Areas

National Disaster Management Research Institute, Ulsan 44538, Korea
Academic Editors: Naser El-Sheimy, Zahra Lari, Adel Moussa, Lars T. Waser and Prasad S. Thenkabail
Received: 26 February 2016 / Revised: 2 June 2016 / Accepted: 14 June 2016 / Published: 22 June 2016
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
View Full-Text   |   Download PDF [7479 KB, uploaded 22 June 2016]   |  

Abstract

Satellite images and aerial images with high spatial resolution have improved visual interpretation capabilities. The use of high-resolution images has rapidly grown and has been extended to various fields, such as military surveillance, disaster monitoring, and cartography. However, many problems were encountered in which one object has a variety of spectral properties and different objects have similar spectral characteristics in terms of land cover. The problems are quite noticeable, especially for building objects in urban environments. In the land cover classification process, these issues directly decrease the classification accuracy by causing misclassification of single objects as well as between objects. This study proposes a method of increasing the accuracy of land cover classification by addressing the problem of misclassifying building objects through the output-level fusion of aerial images and airborne Light Detection and Ranging (LiDAR) data. The new method consists of the following three steps: (1) generation of the segmented image via a process that performs adaptive dynamic range linear stretching and modified seeded region growth algorithms; (2) extraction of building information from airborne LiDAR data using a planar filter and binary supervised classification; and (3) generation of a land cover map using the output-level fusion of two results and object-based classification. The new method was tested at four experimental sites with the Min-Max method and the SSI-nDSM method followed by a visual assessment and a quantitative accuracy assessment through comparison with reference data. In the accuracy assessment, the new method exhibits various advantages, including reduced noise and more precise classification results. Additionally, the new method improved the overall accuracy by more than 5% over the comparative evaluation methods. The high and low patterns between the overall and building accuracies were similar. Thus, the new method is judged to have successfully solved the inaccuracy problem of classification that is often produced by high-resolution images of urban environments through an output-level fusion technique. View Full-Text
Keywords: aerial images; LiDAR; output-level fusion; land cover; building; urban environment aerial images; LiDAR; output-level fusion; land cover; building; urban environment
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

Kim, Y. Generation of Land Cover Maps through the Fusion of Aerial Images and Airborne LiDAR Data in Urban Areas. Remote Sens. 2016, 8, 521.

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