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Comparison of Different Detection Methods for Citrus Greening Disease Based on Airborne Multispectral and Hyperspectral Imagery

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2011 Louisville, Kentucky, August 7-10, 2011  1110570.(doi:10.13031/2013.37734)
Authors:   Xiuhua Li, Won Suk Lee, Minzan Li, Reza Ehsani, Ashish R Mishra, Chenghai Yang, Robert L Mangan
Keywords:   Airborne image, Mahalanobis distance, Minimum distance, Spectral angle mapper, support vector machine

Citrus greening is a devastating disease spread in many citrus groves since first found in 2005 in Florida. Multispectral (MS) and hyperspectral (HS) airborne images of citrus groves in Florida were taken to detect citrus greening infected trees in 2007 and 2010. Ground truthing including ground reflectance measurement and diseased tree confirmation was conducted to build a proper library for HLB infected and healthy canopies. Several classification and spectral mapping methods were investigated to evaluate their applicability to HLB detection. Spectral features derived from both ground reflectance measurement and airborne images were analyzed. Both field, indoor and image spectral analysis showed that HLB infected canopy had higher reflectance in visible range. High positioning error of the ground truth in the 2007 HS image led to detection accuracy of less than 50% in the validation set for every classification methods. In the 2010 images, with better ground truth records, more precise library for HLB infected and healthy canopies were collected and higher classification accuracy was then achieved. Spectral angle mapping (SAM) showed the highest detection accuracy of more than 95% in the training sets of both HS and MS images, but its accuracy in the validation set deceased a lot, to only 55% in HS image and 62% in MS image. The simpler classification method MinDist and MahaDist have somewhat more balanced accuracy rates between the training and validation sets. Support vector machine (SVM) couldnt work properly in HLB detection, but provided a fast, easy and adoptable way to build a mask for tree canopy, so that other background could be easily blocked out for classification.

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