1. 中国农业大学现代精细农业系统集成研究教育部重点实验室，北京 100083 2. 河南科技大学农业工程学院，河南 洛阳 471003 3. Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
Identification of the Citrus Greening Disease Using Spectral and Textural Features Based on Hyperspectral Imaging
MA Hao1,2，3, JI Hai-yan1*, Won Suk Lee3
1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China 2. College of Agricultural Engineering, Henan University of Science and Technology, Luoyang 471003, China 3. Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
摘要： 讨论了基于高光谱成像技术光谱及纹理特征在识别早期柑橘黄龙病中的应用。使用一套近地高光谱成像系统采集了176枚柑橘叶片的高光谱图像作为实验样品，其中健康叶片60枚，黄龙病叶片60枚，缺锌叶片56枚。手工选取每幅叶片高光谱图像的病斑位置作为样品感兴趣区域(regions of interest, ROI)，计算其平均光谱反射率，并以此作为样品的反射光谱，光谱范围为396～1 010 nm。样品光谱分别经过主成分分析(PCA)及连续投影算法(SPA)进行数据降维，再结合最小二乘支持向量机(LS-SVM)分类器建立分类模型。相比原始光谱，由PCA选取的前四个主成分及SPA选取的一组最佳波长组合(630.4，679.4，749.4和899.9 nm)建立的模型拥有更好的分类识别能力，其对三类柑橘叶片平均预测准确率分别为89.7%和87.4%。同时，从被选四个波长的每幅灰度图像中提取6个灰度直方图的纹理特征以及9个灰度共生矩阵的纹理特征再次构建分类模型。经SPA优选的10个纹理特征值进一步提高了分类效果，对三类柑橘叶片的识别正确率达到了100%，93.3%和92.9%。实验结果表明，同时包含光谱信息及空间纹理信息的高光谱图像在柑橘黄龙病的识别中显示了很大的潜力。
关键词：柑橘黄龙病;高光谱成像;分类;纹理特征;连续投影算法
Abstract：In this paper we discussed the application of spectral and textural features in identifying early stage of the citrus greening disease (Huanglongbing or HLB). A total of 176 hyperspectral images of citrus leaves (60 for healthy, 60 for HLB-infected and 56 for zinc-deficient) were captured by using a near-ground hyperspectral imaging system. Regions of interest (ROI) were extracted manually from the part of pathological changes in the images to calculate the average reflectance spectra of each sample as the sample spectra, ranging from 396 to 1 010 nm. The dimensions of the sample spectra were reduced with the algorithms of principal component analysis (PCA) and successive projection analysis (SPA). Classification models were built with the original spectra and candidate variables, the first four PCs selected by PCA and a set of wavelengths (630.5, 679.4, 749.4 and 899.9 nm) selected by SPA. The results based on a classifier of least square-support vector machine (LS-SVM) showed that the classification models built with the candidate variables selected by PCA and SPA had a better performance, achieving 89.7% and 87.4% in terms of average accuracy. In addition, two groups of textural features, extracted from gray images of the four selected wavelengths based on gray-level histogram and gray-level co-occurrence matrix (GLCM), were also used for the classifier. The first ten features ranked by SPA promoted the average accuracy of classifier significantly, achieving 100%, 93.3% and 92.9% for the three class samples respectively. The results of this study indicated that it would be feasible to identify HLB using the image textural features based on selected wavelengths, and it provided a basis for developing a portable HLB detection system with multispectral imaging techniques.
马 淏1，2，3，吉海彦1*，Won Suk Lee3 . 基于高光谱成像技术应用光谱及纹理特征识别柑橘黄龙病 [J]. 光谱学与光谱分析, 2016, 36(07): 2344-2350.
MA Hao1,2，3, JI Hai-yan1*, Won Suk Lee3 . Identification of the Citrus Greening Disease Using Spectral and Textural Features Based on Hyperspectral Imaging. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(07): 2344-2350.
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