基于高光谱遥感的不同品种花生冠层叶面积指数的通用估算模型

为了快速、无损地监测花生冠层LAI,获取其长势信息,于2014年通过大田试验选用5个品种花生作为供试品种,使用ASD FieldSpec HandHeld便携式野外光谱仪采集花生不同生育时期的冠层高光谱数据,同时使用SUNSCAN冠层分析系统实测花生冠层叶面积指数（LAI）,并应用光谱微分技术和统计分析技术,分别分析4种光谱形式和6种植被指数与LAI的相关关系,建立估算模型。结果表明：高光谱反射率及其光谱变换形式中最优波段与LAI的相关性均极显著,其中一阶微分光谱ρ'在793nm波段处构建的估测方程对花生冠层LAI的估算效果最好（r=-0.5391,P＜0.01,RE=0.2497）,其模拟值与实测值的拟合度达极显著水平（R=0.4435,P＜0.01）;一阶微分光谱ρ'在734nm波段处LAI的实测值与模拟值的拟合效果最好（R=0.5485,P＜0.01）。6种植被指数所选的最优组合波段与LAI均通过了0.01水平的显著性检验 （r≥0.5731,P＜0.01）,其中归一化植被指数NDVI[760,976]对花生冠层LAI的估算效果最好（r=-0.6421,P＜0.01,RE=0.2167）,模拟值与实测值的拟合度达极显著水平（R=0.6731,P＜0.01）,且优于ρ'对LAI的估算效果;LAI实测值与模拟值拟合效果最好的为DVI[760,976] （R=0.6893,P＜0.01）。研究结果表明一阶导数光谱和植被指数对花生冠层LAI的估算精度均较高,植被指数的估算精度尤高,研究同时进一步证实了导数光谱和植被指数能较好地消除土壤、大气等环境背景信息的影响。     

不同储存时间和取样部位牛肉颜色的高光谱图像检测

为了探讨高光谱图像技术对不同储存时间和取样部位的牛肉颜色检测的可行性,采集具有代表性的牛肉后腿、里脊和背脊共82个牛肉样品的高光谱图像,并测量其亮度、红度、黄度和饱和度等颜色参数.选取感兴趣区域获取样品代表性光谱,通过选择适宜的谱区范围和预处理方法,建立并评价了预测各颜色参数的偏最小二乘校正模型.对于亮度、红度、黄度和饱和度,校正集的相关系数分别为0.80、0.91、0.91和0.93,校正标准差分别为2.23、1.18、0.82和1.12,预测集的相关系数分别为0.92、0.88、0.87和0.89,预测标准差分别为1.66、1.45、0.80和1.27.研究结果表明,高光谱图像技术可用于快速无损检测不同储存时间下、不同部位的牛肉颜色.     

小麦叶片水分及绿度特征的光谱法诊断

通过ASD FieldSpec光谱仪测定2个小麦品种3个氮肥处理、3个水分处理下的叶片反射光谱,用SPAD-502仪测定叶片的SPAD(绿度指数)值,并称取叶重。用ViewSpec Pro与Matlab软件处理光谱数据,分析光谱参数与叶片含水量及SPAD值的相关关系,从而明确叶片水分及绿度特征的最佳波段或光谱指数。结果表明,水分指数(WI)、水分胁迫指数(MSI)及中红外植被指数(MSVI1)与叶片含水量的相关关系密切且表现稳定,均通过了0.05水平的显著性检验;Fd664(664nm附近处一阶导数光谱值)、SDr/SDb(红边区域一阶微分总和与蓝边区域一阶微分总和的比值)与小麦叶片SPAD值的相关性达到极显著水平,因而利用光谱法诊断和监测小麦叶片水分及绿度特征具有良好的可行性,可为遥感技术应用于精准农业提供依据。     

基于改进的粒子群和蚁群算法的高光谱森林聚类研究

高光谱图像分类可分为监督分类与非监督分类,聚类分析进行非监督分类是一种现今比较受研究者广泛关注的技术.粒子群算法具有自适应、自组织性、可同时进行局部和全局搜索等特点;蚁群算法通过智能个体间不断进行信息交流和传递,具有较强的发现最优解的能力.提出一种基于改进的粒子群和蚁群算法的高光谱图像聚类方法,设计其模型并将其应用在森林类型分类问题上,提高分类精度,减少人工干预.以吉林省汪清林业局为研究区,通过修改粒子群的惯性系数,得出最优解集,然后利用蚁群寻优的过程对阔叶林、针叶林、混交林、水体进行聚类分析,区分精度达到85％证明,该方法能较好地识别森林类型.     

冬小麦不同群体冠层结构的高光谱响应研究

 选择株型差异较大的冬小麦品种，并通过部分品种不同密度试验，分析了冠层结构的两个重要指标叶向值（LOV）与叶面积指数（LAI）与光谱特征参量的关系，同时对20个不同处理进行了聚类分析。结果表明，不同生育阶段株型指标LOV和群体大小指标LAI对光谱的贡献是不同的，前期（以拔节期为主）LOV对光谱的影响要大些，后期主要受LAI的影响；对拔节期包括品种和密度在内的共20个处理进行聚类分析，划分了株型和群体大小的不同组合4个（A-株型直立，群体较小；B-株型直立，群体较大；C-株型披散，群体较小；D-株型披散，群体较大）；拔节期不同类组冠层光谱反射率在400～700 nm范围内反射率由高到低的顺序为A>B>C>D，700～1 150 nm范围内顺序与其相反，并且差异更加显著，此期是利用光谱识别株型的最佳时期；利用近红外波段光谱特征值（拔节期到孕穗期光谱反射率的增量△R890与拔节期反射率R890）做散点图发现，不同类组在散点分布上具有显著差异，通过纵向反射率的差异以及横向两个阶段反射率增量的差异可以对不同群体冠层结构特征进行初步识别。     

Airborne hyperspectral imagery and linear spectral unmixing for mapping variation in crop yield

Spectral unmixing techniques can be used to quantify crop canopy cover within each pixel of an image and have the potential for mapping the variation in crop yield. This study applied linear spectral unmixing to airborne hyperspectral imagery to estimate the variation in grain sorghum yield. Airborne hyperspectral imagery and yield monitor data recorded from two sorghum fields were used for this study. Both unconstrained and constrained linear spectral unmixing models were applied to the hyperspectral imagery with sorghum plants and bare soil as two endmembers. A pair of plant and soil spectra derived from each image and another pair of ground-measured plant and soil spectra were used as endmember spectra to generate unconstrained and constrained soil and plant cover fractions. Yield was positively related to the plant fraction and negatively related to the soil fraction. The effects of variation in endmember spectra on estimates of cover fractions and their correlations with yield were also examined. The unconstrained plant fraction had essentially the same correlations (r) with yield among all pairs of endmember spectra examined, whereas the unconstrained soil fraction and constrained plant and soil fractions had r-values that were sensitive to the spectra used. For comparison, all 5151 possible narrow-band normalized difference vegetation indices (NDVIs) were calculated from the 102-band images and related to yield. Results showed that the best plant and soil fractions provided better correlations than 96.3 and 99.9% of all the NDVIs for fields 1 and 2, respectively. Since the unconstrained plant fraction could represent yield variation better than most narrow-band NDVIs, it can be used as a relative yield map especially when yield data are not available. These results indicate that spectral unmixing applied to hyperspectral imagery can be a useful tool for mapping the variation in crop yield.     

基于高光谱成像技术的青贮玉米饲料pH值无损检测

为实现青贮玉米饲料pH值的快速、无损检测,该研究采用高光谱成像技术建立不同品质青贮玉米饲料pH值的定量检测模型。采集青贮玉米饲料样本936～2 539nm的平均光谱,采用6种预处理方法对青贮玉米饲料平均光谱进行处理,通过建立偏最小二乘回归（partialleastsquaresregression,PLSR）模型得出多元散射校正（multiplicativescatter correction,MSC）和卷积平滑（savitzky-golay,SG）两种预处理方法效果较好,使用竞争性自适应重加权算法（competitive adaptive reweighted sampling,CARS）、变量组合集群分析算法（variable combination population analysis,VCPA）以及迭代保留信息变量（iteratively retains informative variables,IRIV）算法对经MSC和SG卷积平滑预处理光谱进行特征波长提取,利用PLSR和极限学习机（extreme learning machines,ELM）分别建立饲料全波段、特征波长...     

Technique for normalizing intensity histograms of images when the approximate size of the target is known: Detection of feces on apples using fluorescence imaging

A challenge in machine vision is to develop algorithms for detecting a substance with an amorphous shape when measured responses of both the substance and the underlying target have similar characteristics. The challenge is exacerbated when responses for targets are highly variable both across and within discrete target units. An example of this problem is the detection of fecal contamination on apples. Feces on apples can be detected using differential fluorescence responses of contaminated and uncontaminated apple surfaces to UV excitation. However, responses of both feces and apples are due to the presence of chlorophyll-related compounds, and the response of apples varies within and between apples due to natural variation in the distribution of these compounds. We present a technique for normalizing the variability of intensity responses among targets based on a priori knowledge of the image dimensions and the approximate target size. Using this information, a linear equation is derived based on the approximate median intensities of the background and of the target. The median intensities are estimated by calculating a cumulative intensity histogram and using a priori estimates of the percentage of the area in the image occupied by the background and by a generic target. The image is scaled for uniform intensity power using this linear transformation. The benefits of using this technique are demonstrated using hyperspectral fluorescence responses to UV excitation of 48 Golden Delicious and 48 Red Delicious apples artificially contaminated with dilutions of cow feces. Results show that the uniform power transformation normalizes the intensity distributions of apple images and increases the contrast between contaminated and uncontaminated areas on apple surfaces; the coefficients of variation for the average intensities of uncontaminated apple surfaces at 668 nm for Golden and Red Delicious apples were reduced from 39 and 55%, respectively, to 5% for both varieties.     

植被覆盖度对冬小麦冠层光谱的影响及定量化估产研究

为避免土壤背景对冠层光谱的干扰,提高冬小麦定量化估产精度,以河北廊坊中低产田条件下的冬小麦为研究对象,利用ASD Field Spec高光谱仪定点获取冬小麦冠层光谱信息,分析了田间植被覆盖度和冠层NDVI在生育期内的变化,并利用植被覆盖度对冠层NDVI进行了校正。结果表明,通过三基色即RGB、色度和亮度可将数字图像中冬小麦和土壤背景进行分割,从而获得单位面积上冬小麦的覆盖百分比。而通过覆盖度校正后的植被指数即CNDVI能够更具针对性地反映植株冠层氮素信息。在本试验条件下利用灌浆中期的CNDVI与产量进行一元回归或利用全生育期的CNDVI与产量进行多元回归均取得了较好的效果,决定系数分别为0.849和0.853。由于多元回归模型考虑了不同时期的CNDVI的变化,因此模型具有更强的可靠性和稳定性,较适合于冬小麦定量化估产。