基于高光谱图像的黄瓜叶片叶绿素含量及其分布预测研究
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摘要
叶绿素是植物叶片的基本组成物质之一,是植物生长和受环境胁迫等情况的敏感指示器。植物在营养素缺乏或者受到其他外界环境干扰时,都会在叶片叶绿素的含量和分布上表现出来。利用新技术快速、准确和无损的测量植物叶片的叶绿素含量及其分布,替代费时费力的化学分析方法,对植物长势检测与估产、营养诊断与施肥等有重要意义。
本研究以黄瓜叶片为研究对象,探讨应用高光谱图像技术检测叶片叶绿素含量及其分布的方法,主要的研究内容如下：
1按照常规方法提取黄瓜叶片高光谱图像特征参数,如红边参数和植被指数,分析它们与叶绿素含量之间的关系,发现各参数与叶绿素之间存在一定的相关关系,但是相关性均不高。结果表明,红边参数和植被指数反映的信息较单一,而且必须针对具体情况对其修正,有很大的局限性；
2研究主成分分析法和独立分量法提取高光谱图像特征参数,提取黄瓜叶片高光谱图像光谱维的前10主成分分量和前8个独立分量,分别利用多元回归建立叶绿素含量的预测模型,其预测集相关系数R分别达到0.827和0.831。利用逐步回归比较两种方法,独立分量分析法只需要一个独立分量既能与叶绿素含量的相关系数R达到0.766,而主成分分析法需要前3个主成分综合才能得到相似效果。结果表明,利用独立分量法分析黄瓜叶片高光谱图像,预测叶片叶绿素含量的方法是可行的,且独立分量分析比主成分分析更有优势；
3首次根据独立分量分析法得到的叶绿素含量预测模型,计算出黄瓜叶片叶绿素的分布图。结果表明利用分离出来的独立分量计算得到的黄瓜叶片叶绿素含量分布图与实际情况相符合,为植物营养元素亏缺等研究奠定基础；
4用IDL(interface description language)对ENVI (The Environment for Visualizing Images)进行二次开发,开发出了一套高光谱图像数据处理软件,集成了基于批量处理的高光谱图像的标定、感兴趣区域提取、各波长图像及其纹理信息提取、独立分量图计算、数据输出等功能,为快速有效的处理高光谱图像海量数据提供了思路,为科研提供了便利。
本论文对利用高光谱图像技术预测黄瓜叶片叶绿素含量及其分布进行了初步研究,探索了独立分量法在高光谱图像处理中的应用。研究成果不仅为高光谱图像处理探索了新的方法,而且为利用高光谱图像技术快速无损检测植物叶片叶绿素含量及其分布提供了充分的理论依据,对实现植物营养诊断、病虫害判断和植物长势监测等方面的自动化、智能化有着积极的意义。
Chlorophyll is one of basic components of plant. It is sensitive Indicator of plant environmental stress and growth. When nutrition deficiency or other Interference happened on plant, the concentration and distribution of chlorophyll were changed. As an important matter containing nitrogen in plant, the chlorophyll also indicates the nitrogen absorption and utilization. In stead of the time-consuming, costly chemical analysis method, to develop a new approach to rapidly, accurately and non-destructive detect the concentration and distribution of chlorophyll is meaningful.
By taking cucumber leaf as the research object, hyper-spectral imaging was used to detect concentration and distribution of chlorophyll in cucumber leaf in the study. The main research results were as fallows:
1 Red edge parameters and vegetation index were extracted and studied, there was some relationship between the parameters and the concentration of chlorophyll, but it was not significant. The result showed that the information reflected from these parameters was not complete. Those parameters had to be corrected in special condition and could not used to determine the concentration of chlorophyll directly.
2 10 principal components factors (PCs) and 8 independent component factors (ICs) were calculated by PCA (Principal Component Analysis) and ICA (Independent Component Analysis). Multiple linear regression was applied to build the quantitative model with the PCs and ICs. The correlation coefficient (R) was, respectively,0.827 and 0.831. Stepwise regression was applied to compare the effect of ICA and PCA. The result shows that R is up to 0.766 when only using one IC. ICA was better in detecting the concentration of chlorophyll, and had advantages in separating the signal of chlorophyll in hyper-spectral image.
3 Based on the model, distribution map of chlorophyll in cucumber leaf was plotted. The result showed calculating distribution map of chlorophyll in cucumber leaf was feasible, and laid a foundation for the further study.
4 Software for hyper-spectral image processing based on redevelopment of ENVI was designed. In accordance with requirement of agricultural products inspection, the system possessed the following functions:batch calibration, region of interest extraction, feature extraction, independent component image calculates and data output. The Software could provide convenience for data process of hyper-spectral image.
In this paper, detection of concentration and distribution of chlorophyll in cucumber leaf and ICA approaching on the hyper-spectral image were studied. The result provided sufficient theoretical basis for chlorophyll detection and hyper-spectral image processing. It was meaningful for promoting the automation and intelligent in nutrition, plant insect pests and growth vigor diagnosis of plant.
本研究以黄瓜叶片为研究对象,探讨应用高光谱图像技术检测叶片叶绿素含量及其分布的方法,主要的研究内容如下：
1按照常规方法提取黄瓜叶片高光谱图像特征参数,如红边参数和植被指数,分析它们与叶绿素含量之间的关系,发现各参数与叶绿素之间存在一定的相关关系,但是相关性均不高。结果表明,红边参数和植被指数反映的信息较单一,而且必须针对具体情况对其修正,有很大的局限性；
2研究主成分分析法和独立分量法提取高光谱图像特征参数,提取黄瓜叶片高光谱图像光谱维的前10主成分分量和前8个独立分量,分别利用多元回归建立叶绿素含量的预测模型,其预测集相关系数R分别达到0.827和0.831。利用逐步回归比较两种方法,独立分量分析法只需要一个独立分量既能与叶绿素含量的相关系数R达到0.766,而主成分分析法需要前3个主成分综合才能得到相似效果。结果表明,利用独立分量法分析黄瓜叶片高光谱图像,预测叶片叶绿素含量的方法是可行的,且独立分量分析比主成分分析更有优势；
3首次根据独立分量分析法得到的叶绿素含量预测模型,计算出黄瓜叶片叶绿素的分布图。结果表明利用分离出来的独立分量计算得到的黄瓜叶片叶绿素含量分布图与实际情况相符合,为植物营养元素亏缺等研究奠定基础；
4用IDL(interface description language)对ENVI (The Environment for Visualizing Images)进行二次开发,开发出了一套高光谱图像数据处理软件,集成了基于批量处理的高光谱图像的标定、感兴趣区域提取、各波长图像及其纹理信息提取、独立分量图计算、数据输出等功能,为快速有效的处理高光谱图像海量数据提供了思路,为科研提供了便利。
本论文对利用高光谱图像技术预测黄瓜叶片叶绿素含量及其分布进行了初步研究,探索了独立分量法在高光谱图像处理中的应用。研究成果不仅为高光谱图像处理探索了新的方法,而且为利用高光谱图像技术快速无损检测植物叶片叶绿素含量及其分布提供了充分的理论依据,对实现植物营养诊断、病虫害判断和植物长势监测等方面的自动化、智能化有着积极的意义。
Chlorophyll is one of basic components of plant. It is sensitive Indicator of plant environmental stress and growth. When nutrition deficiency or other Interference happened on plant, the concentration and distribution of chlorophyll were changed. As an important matter containing nitrogen in plant, the chlorophyll also indicates the nitrogen absorption and utilization. In stead of the time-consuming, costly chemical analysis method, to develop a new approach to rapidly, accurately and non-destructive detect the concentration and distribution of chlorophyll is meaningful.
By taking cucumber leaf as the research object, hyper-spectral imaging was used to detect concentration and distribution of chlorophyll in cucumber leaf in the study. The main research results were as fallows:
1 Red edge parameters and vegetation index were extracted and studied, there was some relationship between the parameters and the concentration of chlorophyll, but it was not significant. The result showed that the information reflected from these parameters was not complete. Those parameters had to be corrected in special condition and could not used to determine the concentration of chlorophyll directly.
2 10 principal components factors (PCs) and 8 independent component factors (ICs) were calculated by PCA (Principal Component Analysis) and ICA (Independent Component Analysis). Multiple linear regression was applied to build the quantitative model with the PCs and ICs. The correlation coefficient (R) was, respectively,0.827 and 0.831. Stepwise regression was applied to compare the effect of ICA and PCA. The result shows that R is up to 0.766 when only using one IC. ICA was better in detecting the concentration of chlorophyll, and had advantages in separating the signal of chlorophyll in hyper-spectral image.
3 Based on the model, distribution map of chlorophyll in cucumber leaf was plotted. The result showed calculating distribution map of chlorophyll in cucumber leaf was feasible, and laid a foundation for the further study.
4 Software for hyper-spectral image processing based on redevelopment of ENVI was designed. In accordance with requirement of agricultural products inspection, the system possessed the following functions:batch calibration, region of interest extraction, feature extraction, independent component image calculates and data output. The Software could provide convenience for data process of hyper-spectral image.
In this paper, detection of concentration and distribution of chlorophyll in cucumber leaf and ICA approaching on the hyper-spectral image were studied. The result provided sufficient theoretical basis for chlorophyll detection and hyper-spectral image processing. It was meaningful for promoting the automation and intelligent in nutrition, plant insect pests and growth vigor diagnosis of plant.
引文
[1]潘瑞炽,王小菁等.植物生理学[M].北京：高等教育出版社,2008(第6版)：59-62.
[2]马学礼,焦峰.小麦氮营养研究进展[J].现代农业科技,2007,14：9-14.
[3]陈新红,刘凯等.结实期氮素营养和土壤水分对水稻光合特性、产量及品质的影响[J].上海交通大学学报(农业科学版),2004,22(01)：48-53.
[4]冯春才,李东明.有机复合肥对烤烟叶片叶绿素含量的影响[J].2009,29：144-144.
[5]刘元英,罗盛国.连作胁迫下大豆对营养元素的吸收[J].东北农业大学学报,1997,28(3)：209-215.
[6]滕利荣,孟庆繁.生物学基础实验教程(第三版)[M].北京：科学出版社,2008：22-23.
[7]作物光谱分析技术及其在生长监测中的应用[J].中国农学通报,2009,25(23)：182-188.
[8]刘木华,赵杰文,江水泉.高光谱图像在农畜产品品质与安全性检测中的研究现状与展望[J].粮食与食品工业,2004,12：47-49.
[9]王艳,王孝纯.寒地水稻氮磷钾营养诊断技术的研究[J].中国农学通报,2009,25(21)：208-211.
[10]童庆禧,张兵,郑兰芬.高光谱遥感-原理、技术与应用[M].北京：高等教育出版社,2006：370-377.
[11]苏红军,杜培军.高光谱遥感数据光谱特征提取算法与分类研究[J].计算机应用研究,2008,25(2)：390-393.
[12]刘木华,赵杰文,郑建鸿等.农畜产品品质无损检测中高光谱图像技术的应用进展[J].农业机械学报,2005,36(9)：139-143.
[13]邹小波,赵杰文.农产品无损检测技术及数据分析方法[J].北京,中国轻工业出版社,2007：81-95.
[14]HANSEN P M, Schjoerring J K. Reflectance measurement of canopy biomass and nit rogen statue in wheat crops using normalized difference vegetation indices and partial least squares regression[J]. Remote Sensing Environment,2003,86 (4):542-553.
[15]JAGOR.A, Cutler M E J, Curran P J. Estimate canopy chlorophyll concentration from field and airborne spectra[J]. Remote Sensing Environment,1999,68(3):217-224.
[16]Penuelas,J.,Baret,F. Semiempirical indices to access carotenoids chlorophyll:A ritio from leaf spectral reflectance. Photosynthetiea,1995,31:221-230.
[17]Gitelson,A.A.,&Merzlyak,M.N. Signature analysis of leaf reflectance spectra:Algorhtm development for remote sensing of chlorophyll.JoumalofPlantPhysiology,1996,148:494-500.
[18]宋开山,张柏,王宗明等.大豆叶绿素含量高光谱反演模型研究[J].农业工程学报,2006,22(8)：16-21.
[19]刘堂友,匡定波等.湖泊藻类叶绿素a和悬浮物浓度的高光谱量遥感模型研究[J].红外毫米波,2004,23(1)：11-15.
[20]唐延林,黄敬峰等.玉米叶片高光谱特征及与叶绿素、类胡萝卜素相关性的研究[J].玉米科学,2008,16(2)：71-76.
[21]赵宝强.黄瓜缺素症的鉴别及防治[J].广西农业科学,2004,(02)：19.
[22]曾庆连.黄瓜测土配方施肥技术及缺素补救措施[J].农技服务,2008,25(10)：57.
[23]史有艳.黄瓜缺素型黄叶诊断及防治法检索表[J].河北农业,2003,(05).
[24]贾良良,陈新平.作物氮营养诊断的无损测试技术[J].世界农业,2001,6：36-37.
[25]李刚华,丁艳锋.利用叶绿素计(SPAD-502)诊断水稻氮素营养和推荐追肥的研究进展[J].植物营养与肥料学报,2005,11(3):412-416.
[26]Huang J L, Peng S B. Comparison and standardization among chlorophyll meters in their readings on rice leaves[J].Plant Prod. Sci.2004,7(1):97-1001.
[27]张金恒,王珂,王人潮.叶绿素计SPAD-502在水稻氮素营养诊断中的应用[J].西北农林科技大学学报(自然科学版),2003,31(2)：177-1801.
[28]M. S. Kim, A. M. Lefcourt, Y. R. Chen, et al. Multispectral detection of fecal contamination on apples based on hyper-spectral imagery:Part Ⅱ Application of hyper-spectral fluorescence imaging [J]. Transactions of the ASAE,2002,45(6):2039-2047.
[29]R.F. Lu, Y. K. Peng. Hyper-spectral Scattering for assessing Peach Fruit Firmness [J]. Biosystems Engineering,2006,93 (2):161-171.
[30]G. Polder, GW. Heijden, I.T. Young. Tomato sorting using independent component analysis on spectral images [J]. Real Time Imaging,2003,9:253-259.
[31]陈全胜,赵杰文等.利用高光谱图像技术评判茶叶的质量等级[J].光学学报,2008,28(4)：669-674.
[32]赵杰文,刘剑华等.利用高光谱图像技术检测水果轻微损伤[J].农业机械学报,2008,39(1)：106-109.
[33]山东省农业大学科技情报室.日本蔬菜最新栽培技术[M].泰安：山东省出版总社泰安分社,1988：73.
[34]王娟,张桂玲.黄瓜霜霉病的综合防治[J].农技服务,2009,26(11)：62-66.
[35]柴全喜.温室黄瓜生理病害及其防治[J].果蔬种植,2009,26(11)：62-66.
[36]郭兴.温室黄瓜生理病害的发生与预防[J].农村实用技术,2009,(12).
[37]徐玮,汪东风.食品化学实验和习题[M].北京,化学工业出版社,2008：45-46.
[38]Gerrit Polder, Gerie W. A. M, Van Der Heijden et al. Calibration and characterization of imaging spectrographs [J]. J.Near Infrared Spectroscopy,2003,11 (3):193～210.
[39]陈凯.王玉孝.数理统计及其应用[M].北京：水利电力出版社,1986.
[40]Fuan Tsai, William Philpot. Derivative analysis of hyper-spectral data[J]. Remote Sensing Environment,1998,66:41-51.
[41]薛利红,杨林章.采用不同红边位置提取技术估测蔬菜叶绿素含量的比较研究[J].2008,24(9)：165-168.
[42]Pu R, Gong P, Biging G S, et al. Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index[J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41:916-921.
[43]Smith K L, Steven M D, Colls J J. Use of hyper-spectral derivative ratios in the red edge region to identify plant stress responses to gas leak[J]. Remote Sensing of Environment,2004,92:207-217.
[44]杨璐,高永光.利用“红边”估算铜胁迫下玉米叶绿素浓度研究[J].矿业研究与开发,2008,28(5)：77-79.
[45]Ustin S L, Smith M O, Jacquemoud S, et al. Geobotany:Vegetation mapping for earth ciences[M]. In:A. N. Rencz(Ed.), Remote sensing for the earth sciences:Manual of remote sensing.1999,189-247.
[46]Huete AR. A soil vegetation adjusted index (SAVI)[J]. Remote Sensing of Environment, 1988,25,295-309.
[47]Rouse JW, Haas RH, S-chell JA. Monitoring the Vernal Advancements and Retrogradation of Natural Vegetation[J]. NASA/GSFC, Final Report, Greenbelt, MD, USA,1974:1-137
[48]Roujean JL, Breon FM. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements[J]. Remote Sensing of Environment,1995,51,375-384.
[49]Guerric le Maire, Christophe Francois et al. Calibration and validation of hyper-spectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass[J]. Remote Sensing of Environment.2008, 112:3846-3864.
[50]李玉珍,王宜怀.主成分分析及算法.苏州大学学报(自然科学版),2005,21(1)：32-36.
[51]陆婉珍,袁洪福,徐广通等.现代近红外光谱分析技术[M].北京：中国石化出版社,2000.
[52]方如明,蔡健荣,许俐.计算机图像处理技术及其在农业工程中应用[M].北京：清华大学出版社,1999.
[53]孙家广.计算机图形学[M].第3版.北京：清华大学出版社,1998：178-186.
[54]李卓,郭立红.快速图像处理中阈值选取方法的比较研究[J].微计算机信息,2006,22(3-1)：224-225.
[55]Savitzky A, GolayM J E. Smoothing and differentiation of data by simplified least squares procedures [J],Anal.Chem.,1964,36:1627-1639.
[56]王强,束炯.高光谱图像光谱域噪声检测与去除的DSGF方法[J].红外与毫米波,2006,25(1)：29-32.
[57]寻丽娜,方勇华.独立分量分析在高光谱图像舰船检测中的应用[J].计算机仿真,2008,25(9)：196-299.
[58]邹小波,赵杰文.独立分量分析预处理法提高苹果糖度模型预测精度研究[J].分析化学,2006,34(9)：1291-1294.
[59]孙农亮,于雯雯.基于PCA和ICA的虹膜识别方法[J].中国图象图形学报,2008,13(9)：1702-1706.
[60]冯燕,何明一.基于独立成分分析的高光谱图像数据降维及压缩[J].电子与信息学报,2007,29(12)：2871-2875.
[61]张念,刘顺来.独立分量分析(ICA)在地球物理学中的应用[J].计算机与数字工程,2008,36(8)：7-9.
[62]王小敏,曾生根.基于松弛因子的快速独立分量分析算法的遥感图像分类技术[J].计算机工程与应用,2005,7：84-86.
[63]苏令华,衣同胜.基于独立分量分析的高光谱图像压缩[J].光子学报,2008,37(5)：973-976.
[64]Jutten C and Herault J. Blind separation of sources, part Ⅰ:An adaptive algorithm based on neuromimetic architecture. Signal Processing,1991,24(1):1-10.
[65]Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis[J]. IEEE Transactions on Neural Networks 1999,10(3):626-34.
[66]陈国群,付冬梅,常晓辉.基于聚类算法的红外图像伪彩色增强[J].激光与红外,2007,37(4)：384-386.
[67]陈天华.数字图像处理[M].北京：清华大学出版社,2007：171-173.
[68]Sonka M, Hlavac V, Boyle R著.艾海舟,武勃等译.图像处理、分析与机器视觉[M].北京：人民邮电出版社,2003：159-160.
[69]赵文吉.ENVI遥感影像处理专题与实践[M].北京：中国环境科学出版社,2007.
[70]黄长军,胡丽敏.IDL与Matlab的比较分析[J].重庆科技学院学报(自然科学版),2008, 10(3)：140-143.
[71]汤泉,牛铮.基于IDL与ENVI二次开发的遥感系统开发方法[J].计算机应用,2008(26)：270-272.
[72]闫殿武.IDL可视化工具入门与提高[M].北京：机械工业出版社,2003.
[73]韩培友.IDL可视化分析与应用[M].西安：西北工业大学出版社,2006.
[74]ENVI二次开发指南[M].北京：航天星图(北京)有限公司,2006.
[2]马学礼,焦峰.小麦氮营养研究进展[J].现代农业科技,2007,14：9-14.
[3]陈新红,刘凯等.结实期氮素营养和土壤水分对水稻光合特性、产量及品质的影响[J].上海交通大学学报(农业科学版),2004,22(01)：48-53.
[4]冯春才,李东明.有机复合肥对烤烟叶片叶绿素含量的影响[J].2009,29：144-144.
[5]刘元英,罗盛国.连作胁迫下大豆对营养元素的吸收[J].东北农业大学学报,1997,28(3)：209-215.
[6]滕利荣,孟庆繁.生物学基础实验教程(第三版)[M].北京：科学出版社,2008：22-23.
[7]作物光谱分析技术及其在生长监测中的应用[J].中国农学通报,2009,25(23)：182-188.
[8]刘木华,赵杰文,江水泉.高光谱图像在农畜产品品质与安全性检测中的研究现状与展望[J].粮食与食品工业,2004,12：47-49.
[9]王艳,王孝纯.寒地水稻氮磷钾营养诊断技术的研究[J].中国农学通报,2009,25(21)：208-211.
[10]童庆禧,张兵,郑兰芬.高光谱遥感-原理、技术与应用[M].北京：高等教育出版社,2006：370-377.
[11]苏红军,杜培军.高光谱遥感数据光谱特征提取算法与分类研究[J].计算机应用研究,2008,25(2)：390-393.
[12]刘木华,赵杰文,郑建鸿等.农畜产品品质无损检测中高光谱图像技术的应用进展[J].农业机械学报,2005,36(9)：139-143.
[13]邹小波,赵杰文.农产品无损检测技术及数据分析方法[J].北京,中国轻工业出版社,2007：81-95.
[14]HANSEN P M, Schjoerring J K. Reflectance measurement of canopy biomass and nit rogen statue in wheat crops using normalized difference vegetation indices and partial least squares regression[J]. Remote Sensing Environment,2003,86 (4):542-553.
[15]JAGOR.A, Cutler M E J, Curran P J. Estimate canopy chlorophyll concentration from field and airborne spectra[J]. Remote Sensing Environment,1999,68(3):217-224.
[16]Penuelas,J.,Baret,F. Semiempirical indices to access carotenoids chlorophyll:A ritio from leaf spectral reflectance. Photosynthetiea,1995,31:221-230.
[17]Gitelson,A.A.,&Merzlyak,M.N. Signature analysis of leaf reflectance spectra:Algorhtm development for remote sensing of chlorophyll.JoumalofPlantPhysiology,1996,148:494-500.
[18]宋开山,张柏,王宗明等.大豆叶绿素含量高光谱反演模型研究[J].农业工程学报,2006,22(8)：16-21.
[19]刘堂友,匡定波等.湖泊藻类叶绿素a和悬浮物浓度的高光谱量遥感模型研究[J].红外毫米波,2004,23(1)：11-15.
[20]唐延林,黄敬峰等.玉米叶片高光谱特征及与叶绿素、类胡萝卜素相关性的研究[J].玉米科学,2008,16(2)：71-76.
[21]赵宝强.黄瓜缺素症的鉴别及防治[J].广西农业科学,2004,(02)：19.
[22]曾庆连.黄瓜测土配方施肥技术及缺素补救措施[J].农技服务,2008,25(10)：57.
[23]史有艳.黄瓜缺素型黄叶诊断及防治法检索表[J].河北农业,2003,(05).
[24]贾良良,陈新平.作物氮营养诊断的无损测试技术[J].世界农业,2001,6：36-37.
[25]李刚华,丁艳锋.利用叶绿素计(SPAD-502)诊断水稻氮素营养和推荐追肥的研究进展[J].植物营养与肥料学报,2005,11(3):412-416.
[26]Huang J L, Peng S B. Comparison and standardization among chlorophyll meters in their readings on rice leaves[J].Plant Prod. Sci.2004,7(1):97-1001.
[27]张金恒,王珂,王人潮.叶绿素计SPAD-502在水稻氮素营养诊断中的应用[J].西北农林科技大学学报(自然科学版),2003,31(2)：177-1801.
[28]M. S. Kim, A. M. Lefcourt, Y. R. Chen, et al. Multispectral detection of fecal contamination on apples based on hyper-spectral imagery:Part Ⅱ Application of hyper-spectral fluorescence imaging [J]. Transactions of the ASAE,2002,45(6):2039-2047.
[29]R.F. Lu, Y. K. Peng. Hyper-spectral Scattering for assessing Peach Fruit Firmness [J]. Biosystems Engineering,2006,93 (2):161-171.
[30]G. Polder, GW. Heijden, I.T. Young. Tomato sorting using independent component analysis on spectral images [J]. Real Time Imaging,2003,9:253-259.
[31]陈全胜,赵杰文等.利用高光谱图像技术评判茶叶的质量等级[J].光学学报,2008,28(4)：669-674.
[32]赵杰文,刘剑华等.利用高光谱图像技术检测水果轻微损伤[J].农业机械学报,2008,39(1)：106-109.
[33]山东省农业大学科技情报室.日本蔬菜最新栽培技术[M].泰安：山东省出版总社泰安分社,1988：73.
[34]王娟,张桂玲.黄瓜霜霉病的综合防治[J].农技服务,2009,26(11)：62-66.
[35]柴全喜.温室黄瓜生理病害及其防治[J].果蔬种植,2009,26(11)：62-66.
[36]郭兴.温室黄瓜生理病害的发生与预防[J].农村实用技术,2009,(12).
[37]徐玮,汪东风.食品化学实验和习题[M].北京,化学工业出版社,2008：45-46.
[38]Gerrit Polder, Gerie W. A. M, Van Der Heijden et al. Calibration and characterization of imaging spectrographs [J]. J.Near Infrared Spectroscopy,2003,11 (3):193～210.
[39]陈凯.王玉孝.数理统计及其应用[M].北京：水利电力出版社,1986.
[40]Fuan Tsai, William Philpot. Derivative analysis of hyper-spectral data[J]. Remote Sensing Environment,1998,66:41-51.
[41]薛利红,杨林章.采用不同红边位置提取技术估测蔬菜叶绿素含量的比较研究[J].2008,24(9)：165-168.
[42]Pu R, Gong P, Biging G S, et al. Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index[J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41:916-921.
[43]Smith K L, Steven M D, Colls J J. Use of hyper-spectral derivative ratios in the red edge region to identify plant stress responses to gas leak[J]. Remote Sensing of Environment,2004,92:207-217.
[44]杨璐,高永光.利用“红边”估算铜胁迫下玉米叶绿素浓度研究[J].矿业研究与开发,2008,28(5)：77-79.
[45]Ustin S L, Smith M O, Jacquemoud S, et al. Geobotany:Vegetation mapping for earth ciences[M]. In:A. N. Rencz(Ed.), Remote sensing for the earth sciences:Manual of remote sensing.1999,189-247.
[46]Huete AR. A soil vegetation adjusted index (SAVI)[J]. Remote Sensing of Environment, 1988,25,295-309.
[47]Rouse JW, Haas RH, S-chell JA. Monitoring the Vernal Advancements and Retrogradation of Natural Vegetation[J]. NASA/GSFC, Final Report, Greenbelt, MD, USA,1974:1-137
[48]Roujean JL, Breon FM. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements[J]. Remote Sensing of Environment,1995,51,375-384.
[49]Guerric le Maire, Christophe Francois et al. Calibration and validation of hyper-spectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass[J]. Remote Sensing of Environment.2008, 112:3846-3864.
[50]李玉珍,王宜怀.主成分分析及算法.苏州大学学报(自然科学版),2005,21(1)：32-36.
[51]陆婉珍,袁洪福,徐广通等.现代近红外光谱分析技术[M].北京：中国石化出版社,2000.
[52]方如明,蔡健荣,许俐.计算机图像处理技术及其在农业工程中应用[M].北京：清华大学出版社,1999.
[53]孙家广.计算机图形学[M].第3版.北京：清华大学出版社,1998：178-186.
[54]李卓,郭立红.快速图像处理中阈值选取方法的比较研究[J].微计算机信息,2006,22(3-1)：224-225.
[55]Savitzky A, GolayM J E. Smoothing and differentiation of data by simplified least squares procedures [J],Anal.Chem.,1964,36:1627-1639.
[56]王强,束炯.高光谱图像光谱域噪声检测与去除的DSGF方法[J].红外与毫米波,2006,25(1)：29-32.
[57]寻丽娜,方勇华.独立分量分析在高光谱图像舰船检测中的应用[J].计算机仿真,2008,25(9)：196-299.
[58]邹小波,赵杰文.独立分量分析预处理法提高苹果糖度模型预测精度研究[J].分析化学,2006,34(9)：1291-1294.
[59]孙农亮,于雯雯.基于PCA和ICA的虹膜识别方法[J].中国图象图形学报,2008,13(9)：1702-1706.
[60]冯燕,何明一.基于独立成分分析的高光谱图像数据降维及压缩[J].电子与信息学报,2007,29(12)：2871-2875.
[61]张念,刘顺来.独立分量分析(ICA)在地球物理学中的应用[J].计算机与数字工程,2008,36(8)：7-9.
[62]王小敏,曾生根.基于松弛因子的快速独立分量分析算法的遥感图像分类技术[J].计算机工程与应用,2005,7：84-86.
[63]苏令华,衣同胜.基于独立分量分析的高光谱图像压缩[J].光子学报,2008,37(5)：973-976.
[64]Jutten C and Herault J. Blind separation of sources, part Ⅰ:An adaptive algorithm based on neuromimetic architecture. Signal Processing,1991,24(1):1-10.
[65]Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis[J]. IEEE Transactions on Neural Networks 1999,10(3):626-34.
[66]陈国群,付冬梅,常晓辉.基于聚类算法的红外图像伪彩色增强[J].激光与红外,2007,37(4)：384-386.
[67]陈天华.数字图像处理[M].北京：清华大学出版社,2007：171-173.
[68]Sonka M, Hlavac V, Boyle R著.艾海舟,武勃等译.图像处理、分析与机器视觉[M].北京：人民邮电出版社,2003：159-160.
[69]赵文吉.ENVI遥感影像处理专题与实践[M].北京：中国环境科学出版社,2007.
[70]黄长军,胡丽敏.IDL与Matlab的比较分析[J].重庆科技学院学报(自然科学版),2008, 10(3)：140-143.
[71]汤泉,牛铮.基于IDL与ENVI二次开发的遥感系统开发方法[J].计算机应用,2008(26)：270-272.
[72]闫殿武.IDL可视化工具入门与提高[M].北京：机械工业出版社,2003.
[73]韩培友.IDL可视化分析与应用[M].西安：西北工业大学出版社,2006.
[74]ENVI二次开发指南[M].北京：航天星图(北京)有限公司,2006.