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Remote Sens. 2016, 8(6), 521;

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)
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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

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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).

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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.

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