Location: Aerial Application Technology ResearchTitle: Extended spectral angle mapping (ESAM) for citrus greening disease detection using airborne hyperspectral imaging
|LI, HAN - University Of Florida|
|LEE, WON SUK - University Of Florida|
|WANG, KU - China Agricultural University|
|EHSANI, REZA - University Of Florida|
Submitted to: Precision Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/16/2014
Publication Date: 7/5/2014
Citation: Li, H., Lee, W., Wang, K., Ehsani, R., Yang, C. 2014. Extended spectral angle mapping (ESAM) for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture. 15:162-183.
Interpretive Summary: Citrus greening, also known as Huanglongbing (HLB), is a serious citrus disease that causes substantial economic losses to the citrus industry; therefore, early and timely detection of infected trees is important for the management and control of the disease. A novel image classification method, termed as extended spectral angle mapping, for identifying HLB-infected trees using airborne hyperspectral imagery was developed and evaluated. Experimental results showed that the new method had an identification accuracy of 86% and was a significant improvement over existing methods, which have a 64% identification accuracy. This new technique will aid citrus managers and producers in identifying HLB-infected trees from aerial images so that management decisions can be made more quickly.
Technical Abstract: Hyperspectral (HS) imaging is becoming more important for agricultural applications. Due to its high spectral resolution, HS imaging exhibits excellent performance in disease identification of different crops. In this study, a novel method termed ‘extended spectral angle mapping (ESAM)’ was proposed to detect citrus greening disease (Huanglongbing or HLB), which is a very destructive disease of citrus. Firstly, the Savitzky–Golay smoothing filter was used to remove spectral noise within the data. A mask for tree canopy was built using support vector machine, to separate the tree canopies from the background. Pure endmembers of the masked dataset for healthy and HLB infected tree canopies were extracted using vertex component analysis. By utilizing the derived pure endmembers, spectral angle mapping was applied to differentiate between healthy and citrus greening disease infected areas in the image. Finally, most false positive detections were filtered out using red-edge position. An experiment was carried out using an HS image acquired by an airborne HS imaging system, and a multispectral image acquired by the WorldView-2 satellite, from the Citrus Research and Education Center, Lake Alfred, FL, USA. Ground reflectance measurement and coordinates for diseased trees were recorded. The experimental results were compared with another supervised method, Mahalanobis distance, and an unsupervised method, K-means, both of which showed a 63.6% accuracy. The proposed ESAM performed better with a detection accuracy of 86% than those two methods. These results demonstrated that the detection accuracy using HS image could be enhanced by focusing on the pure endmember extraction and the use of red-edge position, suggesting that there is a great potential of citrus greening disease detection using an HS image.