激光雷达中层大气遥感技术

介绍了中科院武汉物理与数学研究所瑞利激光雷达的技术特点，并从其对武汉地区中层大气密度和温度廓线探测的典型结果，给出了该激光雷达对中层大气主要参数的探测能力。     

Sensitivity of depolarized lidar signals to cloud and aerosol particle properties

Measurements from depolarized lidars provide a promising method to retrieve both cloud and aerosol properties and a versatile complement to passive satellite-based sensors. For lidar observations of clouds and aerosols, multiple scattering plays an important role in the scattering process. Monte Carlo simulations are carried out to investigate the sensitivity of lidar backscattering depolarization to cloud and aerosol properties. Lidar parameters are chosen to be similar to those of the upcoming space-based CALIPSO lidar. Cases are considered that consist of a single cloud or aerosol layer, as well as a case in which cirrus clouds overlay different types of aerosols. It is demonstrated that besides thermodynamic cloud phase, the depolarized lidar signal may provide additional information on ice or aerosol particle shapes. However, our results show little sensitivity to ice or aerosol particle sizes. Additionally, for the case of multiple but overlapping layers involving both clouds and aerosols, the depolarized lidar contains information that can help identify the particle properties of each layer.     

A Pseudo-Random Frequency Modulation Continuous Wave Coherent Lidar Using an Optical Field Correlation Detection Method

We propose a new detection method for a pseudo-random frequency modulation continuous wave (RM-CW) coherent lidar. The feature of this method is modulation of local beam with a time delayed pseudo-random sequence. Heterodyne detection and correlation detection between the received beam and the local beam are simultaneously carried out in an optical field. In the RM-CW coherent lidar using the optical field correlation detection method, the received equipment is greatly simplified. We carried out preliminary experiments and demonstrated that the new method is effective for detection of a CW coherent lidar.     

成像技术在食品安全与质量控制中的研究进展

食品质量与安全是政府、食品行业以及消费者十分关注的问题。为了保证食品质量与安全，需要对食品中的风险因子进行检测。传统的分析方法如生物化学方法和仪器分析方法（色谱法、色谱-质谱法）存在前处理比较复杂，耗时，对样品具有破坏性及无法获取目标物空间信息等缺点。因此，开发快速，无损，实时和可视化的检测技术十分重要，这也是食品领域研究的热点。近年来，高光谱成像技术融合了成像和光谱两种技术，可以作为一种用于食品质量和安全评估的非破坏性和实时检测的工具。拉曼光谱成像技术可以同时获得待测物的光谱和空间信息，具有快速，无损和低成本等优点，在食品安全评价和质量控制中也得到了成功应用。质谱成像技术不需要标记和染色，即可实现样品组织表面待测物的可视化和高通量分析。它作为一种分子可视化技术，可以获得食品中营养成分及内、外源性有害物质的空间分布信息，在食品领域也表现出良好的应用前景。本文检索了近几年国内外发表的成像技术在食品研究中的相关文献，介绍了高光谱成像技术、拉曼光谱成像技术和质谱成像技术的原理，并综述了它们在食品安全与质量控制中的应用。此外，本文分析和讨论了这几种成像技术的优缺点，并对成像技术在食品领域的发展前景做出了展望。     

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.     

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.     

非线性拉曼激光雷达测量CO2气体的研究

提出了利用气体的受激拉曼散射(SRS)效应激光雷达光源来探测大气中的CO2气体的新方法,设计出探测大气中CO2气体含量的非线性拉曼增益激光雷达,用Nd:YAG激光器(1064 nm)的三倍频光(354.7 nm)通过分别装有CO2气体和N2气体的拉曼管,分别得到CO2气体和N2气体的受激拉曼散射的一阶斯托克斯线(S1),并用S1线作为雷达的种子发射光源.通过实验得到拉曼管中的气压与S1能量的变化关系,对其优化条件和物理机制进行了分析.该实验方法已经成功测出了大气中CO2气体的回波电压信号.     

基于Raman激光雷达反演大气污染气体浓度几种方法

筊aman激光雷达是探测大气污染气体的一种重要方法,雷达回波反演是其中的重要步骤,反演的关键在于正确得到透过率指数因子。以消光系数为中心的三个方案,介绍了目前已普遍采用的方法、应用532 nm大气消光系数间接求取α(λ₁,z)和α(λ_２,z)。提出了两种新方法：(1) 采用拉曼波长与待测气体相近的参考气体,α(λ₁,z)＝α(λ_２,z);(2) 借助λ₁和λ₂的Mie-Rayleigh散射回波求取指数因子。