Browse > Article

A Topographical Classifier Development Support System Cooperating with Data Mining Tool WEKA from Airborne LiDAR Data  

Lee, Sung-Gyu (인하대학교 정보통신공학과)
Lee, Ho-Jun (인하대학교 정보통신공학부)
Sung, Chul-Woong (인하대학교 정보공학과)
Park, Chang-Hoo (인하대학교 정보통신공학과)
Cho, Woo-Sug (인하대학교 토목공학과)
Kim, Yoo-Sung (인하대학교 정보통신공학부)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.28, no.1, 2010 , pp. 133-142 More about this Journal
Abstract
To monitor composition and change of the national land, intelligent topographical classifier which enables accurate classification of land-cover types from airborne LiDAR data is highly required. We developed a topographical classifier development support system cooperating with da1a mining tool WEKA to help users to construct accurate topographical classification systems. The topographical classifier development support system has the following functions; superposing LiDAR data upon corresponding aerial images, dividing LiDAR data into tiles for efficient processing, 3D visualization of partial LiDAR data, feature from tiles, automatic WEKA input generation, and automatic C++ program generation from the classification rule set. In addition, with dam mining tool WEKA, we can choose highly distinguishable features by attribute selection function and choose the best classification model as the result topographical classifier. Therefore, users can easily develop intelligent topographical classifier which is well fitted to the developing objectives by using the topographical classifier development support system.
Keywords
Airborne LiDAR; Topographical classifier; Dam mining; Feature selection; Automatic rule generation;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 이임평 (2006a), 항공 라이다 데이터의 분할: 점에서 패치로, 한국측량학회지, 한국측량학회, 제24권, 제1호, pp. 111-121.
2 최연웅, 조기성 (2007), Entropy 이론을 이용한 라이다자료 분류기법 연구, 대한토목학회지, 대한토목학회, 제27권2D호, pp. 225-231.
3 Antonarakis, A., Richards, K. and Brasington, J. (2008), Object-based Land Cover Classification Using Airborne LiDAR, Remote Sensing of Environment, Elesevier, Vol. 112, pp. 2988-2998.   DOI   ScienceOn
4 SAS (2010), http://www.sas.com, SAS.
5 Witten, I. H. and Frank, E. (2005), Data Mining: Practical Machine Learning Tools and Technique (2E), Elesevier.
6 이임평 (2006b), 라이다 데이터로부터 지표점 추출을 위한 피쳐 기반 방법, 대한원격탐사학회지, 대한원격탐사학회, 제22권, 제4호, pp. 265-274.   DOI
7 Hasegawa, H. (2006), Evaluations of LiDAR Reflectance Amplitude Sensitivity towards Land Cover Conditions, Bulletin of the Geographical Survey Institute, Japan Geographical Survey Institute, Vol. 53, pp. 43-50.
8 조홍범, 조우석, 박준구, 송낙현 (2008), 항공 라이다 데이터를 이용한 3차원 건물 모델링, 대한원격탐사학회지, 대한원격탐사학회, 제24권, 제2호, pp. 141-152.   DOI
9 Filin, S. and Pfeifer, N. (2006), Segmentation of Airborne Laser Scanning Data Using a Slope Adaptive Neighborhood, ISPRS Journal of Photogrammetry & Remote Sensing, ELSEVIER, Vol. 60, pp. 71-80.   DOI   ScienceOn
10 IBM SPSS (2010), http://www.spss.com, IBM SPSS.
11 Song, J., Han, S., Yu, K. and Kim, Y. (2002), Assessing the Possibility of Land-Cover Classification Using LiDAR Intensity Data, Proceedings of Photogrammetric Computer Vision Symposium, Photogrammetric Computer Vision Symposium, pp. 259-263.
12 Alharthy, Abdullatif and Bethel, James (2003), Automated Road Extraction from LiDAR Data, Proceedings of the ASPRS Annual Conference, American Society for Photogrammetry and Remote Sensing.
13 서용철, 최윤수, 허민 (2009), 항공 레이저 측량 기초와 응용, 대한측량협회.