Remote Sensing Applications and Decision Support

Combining fuzzy set theory and nonlinear stretching enhancement for unsupervised classification of cotton root rot

[+] Author Affiliations
Huaibo Song

Northwest A&F University, College of Mechanical and Electronic Engineering, 22 Xinong Road, Yangling, Shaanxi 712100, China

USDA-ARS Southern Plains Agricultural Research Center, 3103 F and B Road, College Station, Texas 77845, United States

Texas A&M University, Department of Biological and Agricultural Engineering, College Station, Texas 77843, United States

Chenghai Yang

USDA-ARS Southern Plains Agricultural Research Center, 3103 F and B Road, College Station, Texas 77845, United States

Jian Zhang

USDA-ARS Southern Plains Agricultural Research Center, 3103 F and B Road, College Station, Texas 77845, United States

Huazhong Agricultural University, College of Resource and Environment, 1 Shizishan Street, Wuhan, Hubei 430070, China

Dongjian He

Northwest A&F University, College of Mechanical and Electronic Engineering, 22 Xinong Road, Yangling, Shaanxi 712100, China

John Alex Thomasson

Texas A&M University, Department of Biological and Agricultural Engineering, College Station, Texas 77843, United States

J. Appl. Remote Sens. 9(1), 096013 (Aug 26, 2015). doi:10.1117/1.JRS.9.096013
History: Received April 10, 2015; Accepted July 21, 2015
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Abstract.  Cotton root rot is a destructive disease affecting cotton production. Accurate identification of infected areas within fields is useful for cost-effective control of the disease. The uncertainties caused by various infection stages and newly infected plants make it difficult to achieve accurate classification results from airborne imagery. The objectives of this study were to apply fuzzy set theory and nonlinear stretching enhancement to airborne multispectral imagery for unsupervised classification of cotton root rot infections. Four cotton fields near Edroy and San Angelo, Texas, were selected for this study. Airborne multispectral imagery was taken and the color-infrared (CIR) composite images were used for classification. The intensity component was enhanced by using a fuzzy-set based method, and the saturation component was enhanced by a nonlinear stretching image enhancement algorithm. The enhanced CIR composite images were then classified into infected and noninfected areas. Iterative self organization data analysis and adaptive Otsu’s method were used to compare the performance of the proposed image enhancement method. The results showed that image enhancement has improved the classification accuracy of these two unsupervised classification methods for all four fields. The results from this study will be useful for detection of cotton root rot and for site-specific treatment of the disease.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Huaibo Song ; Chenghai Yang ; Jian Zhang ; Dongjian He and John Alex Thomasson
"Combining fuzzy set theory and nonlinear stretching enhancement for unsupervised classification of cotton root rot", J. Appl. Remote Sens. 9(1), 096013 (Aug 26, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.096013


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