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

Precision Agriculture

Volumn 15, Issue 2, 2014, Pages 162-183

  Li, Han ^a   Lee, Won Suk ^b   Wang, Ku ^a   Ehsani, Reza ^c   Yang, Chenghai ^d  

^a CHINA AGRICULTURAL UNIVERSITY   (China)

^b University of Florida   (United States)

^c UNIVERSITY OF FLORIDA   (United States)

^d SOUTHERN PLAINS AGRICULTURAL RESEARCH CENTER   (United States)

Author keywords

Image classification; Precision agriculture; Red edge position; Spectral angle mapping; Vertex component analysis

Indexed keywords

DATA SET; DISEASE; EDUCATION; FILTER; IMAGE CLASSIFICATION; MAPPING; MULTISPECTRAL IMAGE; PRECISION AGRICULTURE; RESEARCH; SATELLITE; SMOOTHING; SPECTRAL RESOLUTION;

UNITED STATES;

CITRUS;

EID: 84896718335     PISSN: 13852256     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11119-013-9325-6     Document Type: Article

References (33)

1. Bhattacharyya, A. (1943). On a measure of divergence between two statistical populations defined by probability distributions. Bulletin of the Calcutta Mathematical Society, 35, 99-109.
2. Boardman, J. W. (1993). Automating spectral unmixing of AVIRIS data using convex geometry concepts (vol. 1). In Summaries 4th Annual JPL Airborne Geoscience Workshop, pp. 11-14.
3. California Department of Food and Agriculture and the USDA. (2012). Citrus Disease Huanglongbing Detected in Hacienda Heights Area of Los Angeles County. Available: http://weblogs. nal. usda. gov/invasivespecies/archives/2012/04/citrus_greening. shtml. Retrieved March 21, 2013.
4. Cho, M. A., & Skidmore, A. K. (2006). A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sensing of Environment, 101(2), 181-193.
5. Chung, K. R., & Brlansky, R. H. (2005). Citrus diseases exotic to Florida: Huanglongbing (citrus greening) (p. 210). Gainesville: IFAS Extension Publication, University of Florida.
6. Clark, R. N., Gallagher, A. J., & Swayze, G. A. (1990). Material absorption band depth mapping of imaging spectrometer data using the complete band shape least-squares algorithm simultaneously fit to multiple spectral features from multiple materials (pp. 176-186). In Proceedings of the Third Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, JPL Publication 90-54.
7. Collins, W. (1978). Remote sensing of crop type and maturity. Photogrammetric Engineering and Remote Sensing, 44, 43-55.
8. Collins, W., Raines, G. L., & Canney, F. C. (1977). Airborne spectroradiometer discrimination of vegetation anomalies over sulphide mineralisation-a remote sensing technique. Geological Society of America, 90th Annual Meeting Programs and Abstracts, 9(7), 932-933.
9. Dawson, T. P., & Curran, P. J. (1998). A new technique for interpolating the reflectance red edge position. International Journal of Remote Sensing, 19(11), 2133-2139.
10. Du, Y., Ives, R., Chang, C.-I., Ren, H., Chang, C.-C., Jensen, J., et al. (2004). A new hyperspectral discrimination measure for spectral similarity. Optical Engineering, 43(8), 1777-1786.
11. Etxeberria, E., Gonzalez, P., Dawson, W., & Spann, T. (2007). An iodine-based starch test to assist in selecting leaves for HLB testing. Gainesville: IFAS Extension Publication, University of Florida, HS-1122.
12. FDACS/DPI. (2011). Sections (TRS) Positive for Huanglongbing (HLB, Citrus Greening) in Florida, Available: http://www. freshfromflorida. com/pi/chrp/greening/StatewidePositiveHLBSections. pdf. Retrieved March 21, 2013.
13. Green, A. A., Berman, M., Switzer, P., & Craig, M. D. (1988). A transformation for ordering multispectral data in terms of image quality with implications for noise removal. Geoscience and Remote Sensing, IEEE Transactions on, 26(1), 65-74.
14. Huang, W., Lamb, D. W., Niu, Z., Zhang, Y., Liu, L., & Wang, J. (2007). Identification of yellow rust in wheat using in situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture, 8(4-5), 187-197.
15. Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Heidebrecht, K. B., Shapiro, A. T., Barloon, P. J., et al. (1993). The spectral image-processing system (SIPS)-interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment, 44(2-3), 145-163.
16. Kumar, A., Lee, W. S., Ehsani, R., Albrigo, L. G., Yang, C. R., & Mangan, L. (2012). Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques. Journal of Applied Remote Sensing, 6(1), 063542.
17. Lee, W. S., Ehsani, R., & Albrigo, L. G. (2008). Citrus greening (Huanglongbing) detection using aerial hyperspectral imaging. In Proceedings of the 9th International Conference on Precision Agriculture, Denver, CO.
18. Li, X., Lee, W. S., Li, M., Ehsani, R., Mishra, A. R., Yang, C., et al. (2012). Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Computers and Electronics in Agriculture, 83, 32-46.
19. Mewes, T., Franke, J., & Menz, G. (2011). Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection. Precision Agriculture, 12(6), 795-812.
20. Nascimento, J. M. P., & Dias, J. M. B. (2005). Vertex component analysis: A fast algorithm to unmix hyperspectral data. IEEE Transactions on Geoscience and Remote, 43(4), 898-910.
21. Nidamanuri, R. R., & Zbell, B. (2011). Transferring spectral libraries of canopy reflectance for crop classification using hyperspectral remote sensing. Biosystem Engineering, 110(3), 231-246.
22. Parker, J. A., Kenyon, R. V., & Troxel, D. E. (1983). Comparison of interpolating methods for image resampling. IEEE Transactions on Medical Imaging MI, 2(1), 31-39.
23. Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Valls, G. C., et al. (2009). Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 113(Supplement 1), S110-S122.
24. Qin, J., Burks, T. F., Ritenour, M. A., & Bonn, W. G. (2009). Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Journal of Food Engineering, 93(2), 183-191.
25. Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627-1639.
26. Smith, G. M., & Milton, E. J. (1999). The use of the empirical line method to calibrate remotely sensed data to reflectance. International Journal of Remote Sensing, 20(13), 2653-2662.
27. Smith, K. L., Steven, M. D., & Colls, J. J. (2005). Plant spectral responses to gas leaks and other stresses. International Journal of Remote Sensing, 26(18), 4067-4081.
28. Texas Department of Agriculture and the USDA. (2012). Texas Department of Agriculture and USDA confirm detection of plant disease that damages citrus trees. Available: http://www. texascitrusgreening. org/news/tda-and-usda-confirm-citrus-greening/. Retrieved March 21, 2013.
29. Ustin, S. L., Roberts, D. A., Gamon, J. A., Asener, G. P., & Green, R. O. (2004). Using imaging spectroscopy to study ecosystem processes and properties. BioScience, 54(6), 523-534.
30. Winter, M. E. (1999). N-findr: An algorithm for fast autonomous spectral endmember determinate in hyperspectral data (pp. 266-277). In Proceedings of SPIE Conference Image Spectrometry V.
31. Yang, C., Everitt, J. H., & Bradford, J. M. (2008). Yield estimation from hyperspectral imagery using spectral angle mapper (SAM). Transactions of the ASABE, 51(2), 729-737.
32. Ye, X., Sakai, K., Sasaoa, A., & Asadac, S. (2008). Potential of airborne hyperspectral imagery to estimate fruit yield in citrus. Chemometrics and Intelligent Laboratory Systems, 90(2), 132-144.
33. Zhang, M., Qin, Z., Liu, X., & Ustin, S. L. (2003). Detection of stress in tomatoes induced by late blight disease in California. International Journal of Applied Earth Observation and Geoinformation, 4(4), 295-310.