显微红外热成像技术在故障定位中的应用

电子元器件是整机的基础,在装备的生产和使用过程中,元器件工作异常将影响整个装备的正常运行,元器件发生故障时,必须迅速、准确地进行故障分析和失效定位,避免军用装备在关键时刻发生故障。显微红外热成像技术是一种对电子元器件的微小面积进行高精度非接触的测量,能够显示元器件的反常热分布,暴露不合理的设计和工艺缺陷。论文介绍了显微红外热像仪的原理及特点,举例说明了显微红外热成像技术在失效分析故障定位中的应用,对元器件的故障失效分析和有效性检测提供了指导作用。     

热释电传感器的非接触式测控系统设计

介绍了利用热释电红外传感器进行非接触式测温的基本原理、红外测温系统结构和信号处理电路的组成。以此为基础,设计了一套非接触式红外测温装置,用该装置测定了移动目标表面温度。在模拟实际场景应用时,实现了对较远距离的运动行人体温的准确测量;装置还增加了高温报警电路和蓝牙无线通信模块,丰富了使用功能。     

基于遗传优化的BP神经网络法在甲烷检测中的应用

针对传统的最小二乘法拟合红外传感器的输出特性曲线时存在误差大、计算复杂,传统的BP神经网络法拟合红外传感器的输出特性曲线时存在网络收敛速度慢、易陷入局部极小的问题,通过分析改进的最小二乘法和改进的基于遗传优化的BP神经网络法的拟合效果,指出改进的BP神经网络法拟合度较高,并给出了改进的BP神经网络法在甲烷体积分数检测中的实验结果。结果表明,该方法能够拟合出理想的曲线,有效提高了红外传感器的检测精度及响应速度。     

红外式触摸手写白板应用设计的关键技术

本文给出了红外式触摸手写白板所实现的功能,分析了其中三项关键技术：屏幕校准、模拟用户操作以及笔迹平滑处理;针对各项关键技术,特别是笔迹平滑处理,提出了相应的解决方案,最后验证了其正确性与实用性。     

复杂背景条件下的红外快速运动小目标检测方法

针对复杂背景下红外快速运动小目标检测问题,提出一种将时域特性分析、小波包变换和管道滤波多帧检测相结合的方法。该方法首先对图像进行时域特性分析确定目标所在帧图像序列,再利用小波包变换对目标所在帧图像进行多尺度分解,提取候选目标,然后通过管道滤波从候选目标中确定小目标的位置,完成对小目标的检测。对实测数据进行仿真实验,结果表明该方法能有效检测红外快速运动小目标。     

MEMS热电堆高温测试系统中单晶材料的红外衰减特性研究

在对PerkinElmer公司的MEMS热电堆红外探测器静态和动态特性实验研究的基础上,本文提出了由MEMS热电堆、红外衰减片、靶体和存储模块组成瞬态表面高温测试装置的方案,对拓宽测温上限的关键元件--红外陶瓷衰减片进行了较深八的研究,用MEMS热电堆测量方法测量了氧化锆陶瓷片红外吸收系数,得出了不同厚度氧化锆（熔点为2715℃）陶瓷片红外衰减倍数曲线,为基于MEMS热电堆的表面高温测试装置的设计提供了依据。     

Radiometric normalization and image mosaic generation of ASTER thermal infrared data: An application to extensive sand sheets and dune fields

Data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) have a significant advantage over previous datasets because of the combination of high spatial resolution (15-90 m) and enhanced multispectral capabilities, particularly in the thermal infrared (TIR) atmospheric window (8-12 μm) of the Earth where common silicate minerals are more easily identified. However, the 60 km swath width of ASTER can limit the effectiveness of accurately tracing large-scale features, such as eolian sediment transport pathways, over long distances. The primary goal of this paper is to describe a method for generating a seamless and radiometrically accurate ASTER TIR mosaic of atmospherically corrected radiance and from that, extract surface emissivity for arid lands, specifically, sand seas. The Gran Desierto in northern Sonora, Mexico was used as a test location for the radiometric normalization technique because of past remote sensing studies of the region, its compositional diversity, and its size. A linear approach was taken to transform adjacent image swaths into a direct linear relationship between image acquisition dates. Pseudo-invariant features (PIFs) were selected using a threshold of correlation between radiance values, and change-pixels were excluded from the linear regression used to determine correction factors. The degree of spectral correlation between overlapping pixels is directly related to the amount of surface change over time; therefore, the gain and offsets between scenes were based only on regions of high spectral correlation. The result was a series of radiometrically normalized radiance-at-surface images that were combined with a minimum of image edge seams present. These edges were subsequently blended to create the final mosaic. The advantages of this approach for TIR radiance (as opposed to emissivity) data include the ability to: (1) analyze data acquired on different dates (with potentially very different surface temperatures) as one seamless compositional dataset; (2) perform decorrelation stretches (DCS) on the entire dataset in order to identify and discriminate compositional units; and (3) separate brightness temperature from surface emissivity for quantitative compositional analysis of the surface, reducing seam-line error in the emissivity mosaic. The approach presented here is valid for any ASTER-related study of large geographic regions where numerous images spanning different temporal and atmospheric conditions are encountered.     

Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery

Vegetation water content is an important parameter for retrieval of soil moisture from microwave data and for other remote sensing applications. Because liquid water absorbs in the shortwave infrared, the normalized difference infrared index (NDII), calculated from Landsat 5 Thematic Mapper band 4 (0.76-0.90 μm wavelength) and band 5 (1.55-1.65 μm wavelength), can be used to determine canopy equivalent water thickness (EWT), which is defined as the water volume per leaf area times the leaf area index (LAI). Alternatively, average canopy EWT can be determined using a landcover classification, because different vegetation types have different average LAI at the peak of the growing season. The primary contribution of this study for the Soil Moisture Experiment 2004 was to sample vegetation for the Arizona and Sonora study areas. Vegetation was sampled to achieve a range of canopy EWT; LAI was measured using a plant canopy analyzer and digital hemispherical (fisheye) photographs. NDII was linearly related to measured canopy EWT with an R² of 0.601. Landcover of the Arizona, USA, and Sonora, Mexico, study areas were classified with an overall accuracy of 70% using a rule-based decision tree using three dates of Landsat 5 Thematic Mapper imagery and digital elevation data. There was a large range of NDII per landcover class at the peak of the growing season, indicating that canopy EWT should be estimated directly using NDII or other shortwave-infrared vegetation indices. However, landcover classifications will still be necessary to obtain total vegetation water content from canopy EWT and other data, because considerable liquid water is contained in the non-foliar components of vegetation.     

Monitoring land surface processes with thermal infrared data: Calibration of SVAT parameters based on the optimisation of diurnal surface temperature cycling features

Land Surface Models (LSM) have been designed to describe water and energy transfers at the soil-vegetation-atmosphere interface, and are therefore essential in many environmental disciplines. These numerical models, driven by the boundary conditions in the atmosphere and in the soil, require adequate knowledge of those vegetation and soil characteristics which are determinant in the characterisation of mass and energy transfers. In view of the fact that, firstly this information is often only partially known, and secondly the transfers are sometimes incorrectly represented, these models can rapidly drift and need to be regularly corrected. To this aim, remote sensing is a promising tool and many studies are currently devoted to the development of assimilation techniques to control their inputs or internal variables. The research presented in this paper contributes to this effort. Its ambition is to explore new methodologies, designed to make use of remote sensing thermal infrared data recorded from space. This study is based on the analysis of links between the characteristics of the diurnal cycle of the surface brightness temperature and the soil-atmosphere interface parameters and variables. The proposed methodology takes advantage of these temperatures cycling features, instead of absolute temperature values, to calibrate the LSM. The results show that the model parameters have a significant impact on the diurnal temperature dynamics, sometimes to a greater extent than on the temperature itself, and that these relationships have diurnal and seasonal variations. As a consequence, the use of TIR data for LSM calibration can be optimised by considering only those parts of the information which are really relevant to parameter calibration.     

RBF神经网络在红外CO_2传感器压力补偿中的应用研究

在红外CO2传感器的测量过程中,环境总压是一个重要的影响因素。在环境总压变化的情况下做好压力补偿得出正确的CO2气体分压值,对提高传感器的测量精度有重要意义。提出一种基于聚类和梯度法的径向基函数(RBF)神经网络方法,利用它的局部逼近特性,建立起其在红外CO2传感器的非线性压力补偿中的网络模型。实验结果表明:该应用收到了良好的效果。