Classification of LiDAR Images Fused With Aerial Optical Images Using Ensemble Classifier AdaBoost.MH and Post-processing BFS

Authors

  • Desta Sandya Prasvita STIMIK ESQ
  • Aniati Murni Arymurthy University of Indonesia, Faculty of Computer Science, Depok, 16422, Indonesia

Keywords:

LiDAR, AdaBoost.MH, Post-processing, Breadth First Search (BFS)

Abstract

The objective of this research is to propose a method in order to increase classification performance of LiDAR images fused with aerial optical images. Classification method in use is popular multiclass ensemble classifier method, AdaBoost.MH. The Weak classifier in use of AdaBoost.MH is Hamming Trees. Then, the post-processing method is conducted to remove the noise with Breadth First Search (BFS) algorithm. Post-processing increase the accuracy to 0.96% and able to reduce the noise of classification result. This research is able to increase the accuracy on previous research by 94.96%

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Published

2018-11-07

How to Cite

Prasvita, D. S., & Arymurthy, A. M. (2018). Classification of LiDAR Images Fused With Aerial Optical Images Using Ensemble Classifier AdaBoost.MH and Post-processing BFS. International Journal Technology and Business, 1(1), 10–16. Retrieved from http://ijtb.esqbs.ac.id/index.php/IJTB/article/view/178

Issue

Vol. 1 No. 1 (2017): IJTB (International Journal Of Technology And Business)

Section

Articles