Interpretation of irrigation methods using airborne thermal infrared imagery

In areas of limited water resources for agriculture, management decisions could be enhanced by the availability of additional data about the amount and type of irrigation systems employed. Imagery produced using an HRB Singer AN/A AS 14 optical-electronic thermal infrared scanning system was found to be of great use for the interpretation of irrigation methods. Comparison of the interpreted irrigation method with ground information show agreement within 18 per cent.     

Achieving downscaling of Meteosat thermal infrared imagery using artificial neural networks

This study presents the successful application of artificial neural networks (ANNs) for downscaling Meteosat Second Generation thermal infrared satellite imagery. The scope is to examine, propose, and develop an integrated methodology to improve the spatial resolution of Meteosat satellite images. The proposed approach may contribute to the development of a general methodology for monitoring and downscaling Earth’s surface characteristics and cloud systems, where there is a clear need for contiguous, accurate, and high-spatial resolution data sets (e.g. improvement of climate model input data sets, early warning systems about extreme weather phenomena, monitoring of parameters such as solar radiation fluxes, land-surface temperature, etc.). Moderate Resolution Imaging Spectroradiometer (MODIS) images are used to validate the downscaled Meteosat images. In terms of the ANNs, a multilayer perceptron (MLP) is used and the results are shown to compare favourably against a linear regression approach.     

On the analysis of thermal infrared imagery: The limited utility of apparent thermal inertia

A spectral window in the thermal infrared permits observations of surface temperature by satellite radiometry. The Heat Capacity Mapping Mission (HCMM) acquired 10–12 μm data at times of day favorable for estimation of surface thermal properties and the surface energy budget. Two variables, surface wetness, which controls evaporation and hence mean surface temperature, and thermal inertia, which relates the diurnal excursion of surface temperature to ground heat flux, are responsible for most observed temperature variability. These variables may be estimated from the mid night (2:30 a.m.) and early afternoon (1:30 p.m.) data from the HCMM or from the afternoon NOAA satellites. However, the HCMM data product, “apparent thermal inertia,” is potentially misleading in agricultural areas because surface evaporation reduces the amplitude of the soil heat flux compared to the amplitude in dry areas. Thus apparent thermal inertia should not be used in regions having variability in surface moisture.     

Early fire detection using non-linear multitemporal prediction of thermal imagery

This paper presents a sub-pixel thermal anomaly detection method based on predicting background pixel intensities using a non-linear function of a plurality of past images of the inspected scene. At present, the multitemporal approach to thermal anomaly detection is in its early development stage. In case of space-borne surveillance the multitemporal detection is complicated by both spatial and temporal variability of background surface properties, weather influences, viewing geometries, sensor noise, residual misregistration, and other factors. We use the problem of fire detection and the MODIS data to demonstrate that advanced multitemporal detection methods can potentially outperform the operationally used optimized contextual algorithms both under morning and evening conditions.     

Pedestrian detection and tracking in infrared imagery using shape and appearance

In this paper, we present an approach toward pedestrian detection and tracking from infrared imagery using joint shape and appearance cues. A layered representation is first introduced and a generalized expectation-maximization (EM) algorithm is developed to separate infrared images into background (still) and foreground (moving) layers regardless of camera panning. In the two-pass scheme of detecting pedestrians from the foreground layer: shape cue is first used to eliminate non-pedestrian moving objects and then appearance cue helps to locate the exact position of pedestrians. Templates with varying sizes are sequentially applied to detect pedestrians at multiple scales to accommodate different camera distances. To facilitate the task of pedestrian tracking, we formulate the problem of shot segmentation and present a graph matching-based tracking algorithm that jointly exploits the shape, appearance and distance information. Experimental results with both OSU Infrared Image Database and WVU Infrared Video Database are reported to demonstrate the accuracy and robustness of our algorithm.     

Using satellite thermal infrared imagery to study boundary layer structure in an Antarctic katabatic wind region

We use snow surface temperatures obtained from thermal infrared (TIR) satellite imagery, together with radiosonde profiles of free-air temperature and high-resolution topographic data to study the thermal structure of the atmospheric boundary layer in a coastal region ofEast Antarctica. Surface temperatures over a coastal ice shelf are shown to be significantly lower than those observed on the lower part of the adjoining coastal slopes as a result of the strong surface temperature inversion that forms over the ice shelf. Between 400 and 1500 m elevation the surface temperature lapse rate is close to the dry adiabatic value while the free-air temperature profile is significantly stable over this height range. We argue that this implies that the strength of the surface inversion increases with increasing elevation. Above 1500 m the surface temperature lapse rate becomes significantly superadiabatic and the coldest surface temperatures are found a few 10s of kilometres inland of the highest topography. The technique may prove useful for studying boundary layer structure in other regions of Antarctica where suitable high-resolution topographic data are available.     

复杂热红外监控场景下行人检测

目的 复杂热红外监控场景中的行人检测问题是计算机视觉领域的重要研究内容之一,是公共安全、灾难救援以及智慧城市等实际应用中的重要基础任务。现今的热红外行人检测算法大多依据图像中人体目标的灰度值高于场景环境这一假设,导致当环境温度升高热红外图像发生灰度值反转时行人检测率较低。为提高行人检测系统在不同场景中的鲁棒性以及行人目标检测率,提出一种面向热红外监控场景的基于频域显著性检测的全卷积网络行人目标检测算法。方法 该算法首先对热红外图像进行基于频域的显著性检测,生成对行人目标全覆盖的显著图;然后结合热红外原图像生成感兴趣区域图作为输入,以行人目标概率图为输出,搭建全卷积网络;最后,对热红外行人检测系统进行端对端训练,获取网络输出的行人目标概率图,进而实现行人目标检测。结果 论文使用俄亥俄州立大学建立的红外视频数据集OTCBVS中的OSU热红外行人数据库对算法进行验证,与目前5种较为成熟的算法进行对比。实验结果表明,本文算法可以在各种场景中准确检测出行人目标,以MR-FP（丢失率—假阳率）为对比依据,本文算法7%的平均丢失率低于其他算法,具有更高的检测率,对热红外图像中的灰度值反转问题具有更好的鲁棒性。结论 本文提出一种面向热红外监控场景的基于频域显著性检测的全卷积网络行人目标检测算法,在实现检测算法端对端训练的同时,提高了其对各种复杂场景的鲁棒性以及行人目标检测率,提升热红外监控系统中行人目标检测性能。     

Cloud detection from thermal infrared images using a segmentation technique

In this paper, a fast and accurate method is proposed for two cloud detection tests on thermal infrared (IR) data obtained from satellite images over sea; an IR gross cloud test and a spatial coherence test. The proposed method is based on a regional segmentation technique. After the segmentation of an IR image, small regions were regarded as cloudy due to their high spatial variability in temperature. This technique preserved the spatial resolution of the detected cloud image which would be degraded by the conventional spatial coherence test. It also reduced the computation dramatically, compared to the conventional spatial coherence test. An accurate temperature threshold between clear sea and clouds was determined directly from the segmented image. This post-determined threshold was found to be more accurate than pre-determined temperature thresholds. Since this algorithm does not require any human interaction, it can be combined with other tests in an automatic cloud detection algorithm.     

被动式红外成像气体目标智能检测算法及量化研究进展

在“双碳目标”、安全发展的时代背景下,工业气体泄漏检测因牵涉经济资源、生态环境、生产安全而成为国内外普遍关注的重要问题.气体被动式红外成像因其动态直观、可进行非接触式大范围遥测的特点而被视为检测泄漏的有效工具.在这一技术中,气体目标智能化检测及泄漏量化是两个核心的研究热点问题,且在未来一段相当长的时间内仍会是两项挑战.鉴于此,针对这两方面的研究进行综述.首先,分析基于被动式红外成像气体检测技术的原理,探究影响成像检测结果的关键因素及其作用形式;其次,将气体智能化检测算法按图像识别、视频分类、目标检测、图像分割等不同计算机视觉任务予以分类整理;再次,分别介绍气体量化任务中柱密度、路径浓度、泄漏率三者的测量方法,并重点强调不确定性分析对量化结果的重要性;最后,对气体智能化检测及量化研究中存在的问题提供一些潜在的解决方案,并展望了气体被动式红外成像技术未来的研究方向.     

CBERS-02IRMSS热红外数据及UTAE算法在城市热岛研究中的应用

文中使用城市热异常信息提取算法(urban thermal anomaly extraction,UTAE)提取城市热岛信息.该算法使用不同大小的滑动窗口来探测整幅图像,根据稳健估计的方法设定地表温度高于全部像元以及窗口内像元温度统计均值加标准差的像元为热异常像元,并将滑动窗口的范围设置在3×3至9×9个像元之间,根据像元被记为热岛像元的次数是否为0来区别热岛区与非热岛区,在热岛区域内根据次数的多少来决定热岛强度,具有动态阈值的特点和无偏特性.研究结果表明,在城市热岛范围的提取上,2004年2月13日长沙市热岛面积在27.21—33.98km2之间;在城市热岛强度和尺度效应分析上,宏观尺度的研究应选用较大的窗口来体现城—郊热岛关系以及热岛发展的总体趋势,而小窗口在微观分析城区内热岛的具体分布、与下垫面的关系以及驱动因子方面较有优势.最后,对UTAE算法在中巴地球资源卫星后继星数据上的应用做出了展望.     

红外热成像技术在海上平台的应用研究

随着中国工业化进程的加快,石油石化工业对整个国民经济的支撑作用日愈凸现,与此同时,对石油化工设备的安全性能和节源降耗也提出了越来越高的要求,而海洋石油和海上平台又有着其自身的重要性和特殊性,在传统条件下,我们无法对某些设备进行直接测温,不能及时发现设备的电力问题、轴承过热等现象。在这种情况下,应用红外热成像技术可以通过不接触式测量温度、分析热成像图片等方法解决设备故障。本文着重论述了红外检测适用于海上平台的原因,并结合海洋石油和海上平台的特点论述了检测的实际应用结果及意义。     

变压器漏磁热损的光纤Bragg光栅检测与温升特性分析

变压器的漏磁场强度随着变压器容量的增大而增大,漏磁场强度越大在变压器各结构中引起的漏磁损耗越大,导致变压器运行效率低下,进而影响变压器的正常运行。对35 kV变压器进行试验,将漏磁产生最大处的温度与油箱产生顶端处的温度进行了对比,试验得出当开风机时该处与油箱顶端处的最高温度差为8.7℃、当负荷降至1倍功率时温差为2.9℃,它们的实时温度曲线图与变压器的运行一致,产生了局部温升的现象。通过光纤Bragg光栅（ FBG）检测温度的变化反映变压器漏磁的情况,从而实现了对变压器漏磁的实时在线监测。     

基于可见光-热红外视觉监控的车间工人跌倒检测算法

为了解决工厂车间视觉监控存在噪声干扰、光线变化、目标遮挡等问题,提出一种基于多模态视觉监控的工人跌倒检测算法.首先,采用热像仪和可见光相机获取车间内全天候监控图像,结合自适应滤波模型对图像进行降噪处理,以抑制环境噪声对监控图像的干扰;然后,构建一种改进的人体姿态特征提取网络,通过融合串联时间帧合并模块和位姿残差模块,以简化目标检测的特征图尺度,实现监控图像中工人区域被部分遮挡时姿态的实时、可靠预测;最后,设计人体轴线倾角、人体外接矩形框长宽比以及双膝盖点移动速度作为工人跌倒判别性特征,进而实现车间内工人的跌倒判别.在自建数据集和公开数据集上对所提出方法进行验证,实验结果表明,所提出算法的跌倒检测精度分别为95.6%和96.3%,与对比算法相比具有更好的准确性和实时性.     

A new thermal infrared and visible spectrum images-based pedestrian detection system

In this paper, we propose a hybrid system for pedestrian detection, in which both thermal and visible images of the same scene are used. The proposed method is achieved in two basic steps: (1) Hypotheses generation (HG) where the locations of possible pedestrians in an image are determined and (2) hypotheses verification (HV), where tests are done to check the presence of pedestrians in the generated hypotheses. HG step segments the thermal image using a modified version of OTSU thresholding technique. The segmentation results are mapped into the corresponding visible image to obtain the regions of interests (possible pedestrians). A post-processing is done on the resulting regions of interests to keep only significant ones. HV is performed using random forest as classifier and a color-based histogram of oriented gradients (HOG) together with the histograms of oriented optical flow (HOOF) as features. The proposed approach has been tested on OSU Color-Thermal, INO Video Analytics and LITIV data sets and the results justify its effectiveness.

     

Fusion of thermal infrared and visible spectra for robust moving object detection

The detection of moving objects is a crucial step for many video surveillance applications whether using a visible camera (VIS) or an infrared (IR) one. In order to profit from both types, several fusion methods were proposed in the literature: low-level fusion, medium-level fusion and high-level fusion. The first one is the most used for moving objects’ detection in IR and VIS spectra. In this paper, we present an overview of the different moving object detection methods in IR and VIS spectra and a state of the art of the low-level fusion techniques. Moreover, we propose a new method for moving object detection using low-level fusion of IR and VIS spectra. In order to evaluate quantitatively and qualitatively our proposed method, three series of experiments were carried out using two well-known datasets namely “OSU Color-Thermal Database” and “INO-Database”; the results of these evaluations show promising results and demonstrate the effectiveness of the proposed method.     

一种新型磁阻式传感器在漏磁检测中的应用

与传统的磁性传感器相比,巨磁阻传感器具有灵敏度高、可靠性好、测量范围宽、体积小、价格低等优点。随着纳米技术的发展,巨磁阻传感器必将成为这些传统磁性传感器的替代品。介绍了巨磁阻传感器的原理及其在缺陷漏磁信号检测中的应用,表明采用巨磁阻传感器的检测系统可以检测到管道壁上的微小缺陷。     

Thermophysical algebraic invariants from infrared imagery forobject recognition

An important issue in developing a model-based vision system is the specification of features that are invariant to viewing and scene conditions and also specific, i.e., the feature must have different values for different classes of objects. We formulate a new approach for establishing invariant features. Our approach is unique in the field since it considers not just surface reflection and surface geometry in the specification of invariant features, but it also takes into account internal object composition and state which affect images sensed in the nonvisible spectrum. A new type of invariance called thermophysical invariance is defined. Features are defined such that they are functions of only the thermophysical properties of the imaged objects. The approach is based on a physics-based model that is derived from the principle of the conservation of energy applied at the surface of the imaged object     

Effective wavenumber for thermal infrared bands-application to Landsat-TM

In this paper we suggest that brightness temperature from the band radiance measured by a thermal sensor using Planck's law can be obtained along with effective parameters adequate for a given band, and a given temperature range. We propose an accurate algorithm to obtain these parameters (effective wavenumber or effective wavelength), which is based on Taylor's expansion of the exponential function that appears in Planck's equation. The dependence of the effective wavenumber on temperature has been investigated and the advantages of working with large temperature ranges have been analysed. We have compared the effective wavenumbers obtained with this algorithm to the parameters provided by NOAA/NESDIS and the maximum difference obtained has been 0.5 cm-1. The method has been applied to Landsat-Thematic Mapper band-6 and the results indicate that the error in temperature determination of the method is less than 0.01 K. A comparison with other published algorithms is also included.