Research Information System
Journal of Geodesy and Geoinformation, no.1, pp.105-112, 2012 (Peer-Reviewed Journal)
Random Forest (RF) algorithm is known to be one of the most efficient classi?cation methods. Due to its inherent interdisciplinary nature, it draws researchers from different backgrounds. This study aims at investigating the performance of RF algorithm using multispectral satellite images having different spatial resolutions and scene characteristics. The satellite images used include Ikonos and QuickBird images with four multispectral bands. Ikonos image taken in 2003 covers mainly urban area, whereas QuickBird images acquired in 2005 and 2008 covers both urban and rural areas, respectively. QuickBird image taken in 2005 also contains noisy patterns over Black Sea due to waves resulting from windy weather. To evaluate the performance
of RF, the classification results are compared with the results obtained from Gentle AdaBoost (GAB), Support Vector Machine (SVM) and Maximum Likelihood Classification (MLC) algorithms. Preliminary results indicate that RF gives higher classification accuracies than other methods. For Ikonos image over urban area, the results show that RF algorithm gives 10% higher classification accuracy than SVM, whereas GAB algorithm has the lowest classification accuracy (14 % lower than RF). For QuickBird image (taken in 2008) of rural area, RF gives the best result compared to the others. Also, for QuickBird image containing noisy pattern, RF has around 11% higher overall accuracy than SVM.