基于测地线距离和正交投影的端元提取算法

端元提取是高光谱遥感图像混合像元分解的关键步骤。传统线性端元提取方法忽略了像元内地物的非线性混合因素,制约了混合像元分解精度的提升。针对高光谱图像数据的非线性结构,提出一种基于测地线距离的正交投影端元提取算法,将测地线距离引入端元单体提取过程,利用正交投影方法逐个提取端元。为了降低测地线距离计算量,在端元提取前先利用自动目标生成方法和无约束最小二乘法对原始高光谱数据进行数据约减。模拟和真实高光谱图像实验表明,该方法能够表征光谱数据中非线性因素,端元提取结果优于传统自动目标生成端元提取方法。     

基于高光谱激光雷达的滞尘叶片光谱特征分析

城市绿植为城市生态系统提供自净功能, 起到净化空气以及滞尘降尘等多种环境保护作用, 而滞尘等因素也 会对绿植产生影响。为了研究滞尘对城市绿植叶片光谱特征的影响, 采集了四种常绿绿植 (八角金盘、石楠、香樟和 玉兰) 叶片样本, 使用高光谱激光雷达系统获取高光谱点云数据, 分析了滞尘对叶片光谱特征的影响。分析结果表明: 对于不同种类叶片, 滞尘对可见光波段反射率均有较大影响; 对于同种类叶片, 滞尘对近红外波段的反射率差异影响 较大, 可见光波段的反射率差异为 1.21%∼3.41%, 近红外为 1.76%∼8.49%; 线性四点内插法计算和光谱导数分析表明 滞尘对四种叶片的红边位置无显著影响; 四种叶片的叶面水含量指数 (LWI) 对滞尘的响应程度最小 (均小于 3.7%), 而 比值植被指数 (RVI) 对滞尘的响应程度最大 (除香樟外, 均大于 20.0%), 红边指数 (SDr)、简单比值指数 (SR) 和叶面叶 绿素指数 (LCI) 的响应程度稳定性较差。进一步建立了滞尘植被指数和响应程度的线性相关性拟合模型并进行了检 验, 其中以 LCI 为自变量建立的模型为最稳定拟合模型, 可表示为 y = −1.527x + 0.6597, 决定系数约为 0.88。     

基于约束空间光谱联合的亚像素定位方法北大核心CSCD

针对高光谱遥感图像,提出了一种约束空间光谱的亚像素定位方法。传统的亚像素定位方法以解混的结果作为输入,可能无法充分利用高光谱图像丰富的光谱信息。本文所提出的基于约束空间光谱联合的亚像素定位方法(constraint spatial-spectral subpixel mapping,CSSSM),利用下采样将像素丰度与亚像素丰度显式联系起来,代入线性解混模型得到亚像素丰度求解的新模型。在求解过程中,通过添加稀疏性约束与平滑性约束,以限制亚像素丰度的解空间,亚像素丰度求解更精确。其中,针对亚像素丰度稀疏性先验采用重加权1范数作为新的约束,并自适应地更新权重;针对亚像素丰度空间先验信息则采用全变分(total variational,TV)正则化作为约束,然后使用乘法迭代算法求解亚像素丰度,最后利用赢者通吃的策略进行类别确定。在两个合成数据集上进行了实验,结果表明,本方法能够进一步提高亚像素定位的精度。     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

Near-infrared (NIR) hyperspectral imaging system was used to detect five concentration levels of ochratoxin A (OTA) in contaminated wheat kernels. The wheat kernels artificially inoculated with two different OTA producing Penicillium verrucosum strains, two different non-toxigenic P. verrucosum strains, and sterile control wheat kernels were subjected to NIR hyperspectral imaging. The acquired three-dimensional data were reshaped into readable two-dimensional data. Principal Component Analysis (PCA) was applied to the two dimensional data to identify the key wavelengths which had greater significance in detecting OTA contamination in wheat. Statistical and histogram features extracted at the key wavelengths were used in the linear, quadratic and Mahalanobis statistical discriminant models to differentiate between sterile control, five concentration levels of OTA contamination in wheat kernels, and five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels. The classification models differentiated sterile control samples from OTA contaminated wheat kernels and non-OTA producing P. verrucosum inoculated wheat kernels with a 100% accuracy. The classification models also differentiated between five concentration levels of OTA contaminated wheat kernels and between five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels with a correct classification of more than 98%. The non-OTA producing P. verrucosum inoculated wheat kernels and OTA contaminated wheat kernels subjected to hyperspectral imaging provided different spectral patterns.     

Effective detection of benzoyl peroxide in flour based on parameter selection of Raman hyperspectral system

Penetration depth and spatial resolution of Raman hyperspectral imaging system were studied for effective detection of benzoyl peroxide in flour. The determinations of parameters were achieved by using the single-band background-correct image of a benzoyl peroxide Raman characteristic band and a simple threshold method. The selected parameters were used to detect mixture samples with different concentrations. Percentage of detected benzoyl peroxide pixels was positively correlated to its concentration. The result shows that parameters selected in this study are effective for the detection of benzoyl peroxide additive in flour and can be used for quantitative analysis in the future.     

热红外高光谱成像技术的研究现状与展望

高光谱成像具有精细的光谱分辨能力,在热红外谱段实施高光谱成像对目标探测与识别有显著效果.与国外相比,我国在该领域的研究还相对薄弱,应用部门的研究主要基于国外数据,国内尚未有成熟的仪器.对国内外研究现状进行了详细调研,并结合目前国内已经布局的研究项目对该领域未来的发展进行了展望,对我国发展高性能空间红外光谱成像技术具有一定意义.     

基于加权模糊C均值算法改进的高光谱图像分类方案设计

为了有效改善高光谱图像数据分类的精确度,减少对大数目数据集的依赖,在原型空间特征提取方法的基础上提出一种基于加权模糊C均值算法改进型原型空间特征提取方案。该方案通过加权模糊 C 均值算法对每个特征施加不同的权重,从而保证提取后的特征含有较高的信息量。实验结果表明,与业内公认的原型空间提取算法相比 该方案在相对较小的数据集下,其性能仍具有较为理想的稳定性,且具有相对较高的分类精度,这样子就大大降低了对数据集样本数量的依赖性,同时改善了原型空间特征方法的效率。     

Quantitative luminescence mapping of Cu(In,Ga)Se2 thin‐film solar cells

We investigate photoluminescence and electroluminescence (PL and EL) emission images from Cu(In,Ga)Se₂‐based solar cells by means of a Hyperspectral Imager. Using the generalized Planck's law, maps of the effective quasi‐Fermi level splitting Δμ_eff in absolute values are obtained. A good agreement is found between the spatially averaged splitting in PL and the global open‐circuit voltage. However, from a local carrier transport discussion, we conclude that the equality does not hold locally. The spatial variations are rather attributed to local depth variations of the quasi‐Fermi level splitting due to material properties spatial fluctuations. By comparing PL and EL emissions, we discuss qualitatively the local effective lifetimes and collection efficiencies. Copyright © 2014 John Wiley & Sons, Ltd.     

基于ROI编码的干涉超光谱图像近无损压缩

针对干涉超光谱成像仪所成图像的特点,提出了一种基于ROI（Region of Interest,感兴趣区域）编码的干涉超光谱图像序列压缩方法.利用帧内差值变换,结合矩形ROI编码,在提高遥感图像压缩比的同时,使压缩后的重建图像质量达到近无损的技术要求.实验结果表明,这种压缩方法在干涉超光谱图像压缩中当相对光谱二次误差为2.04％时,干涉图像的压缩比达到9.66∶1.