‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging

‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne... Hyperspectral (HS) imaging is becoming more important for agricultural applications. Due to its high spectral resolution, it 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging

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Publisher
Springer US
Copyright
Copyright © 2013 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-013-9325-6
Publisher site
See Article on Publisher Site

Abstract

Hyperspectral (HS) imaging is becoming more important for agricultural applications. Due to its high spectral resolution, it 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.

Journal

Precision AgricultureSpringer Journals

Published: Jul 18, 2013

References

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