基于BP神经网络的水蒸气红外透过率仿真

针对在计算大气透过率方法中利用经验公式误 差大和专业软件复杂低效等问题,提出了一种基于 贝叶斯正则化BP(back propagation)神经网络的水蒸气红外透过率的计算方法。利用BP 神 经网络良好的非线性拟合特点,建 立大气参数与水蒸气透过率之间的关系模型。以实测温度、压强和湿度作为输入向量,中红 外平均水蒸气 透过率作为输出,构建3-7-1式的BP神经网络。仿真结果表明: 在相同的大气参数下,与逐线积分法相比, 本文方法在运算过程大幅简化的同时相对误差很小;与经验公式法相比,本文方法对透过率 的计算精度大幅提升。     

Impact of the atmospheric transmittance and total water vaporcontent in the algorithms for estimating satellite sea surfacetemperatures

Sea surface temperature (SST) algorithms for NOAA AVHRR data can determine SST with rms values of 0.7 K on a global basis. However, this figure is not compatible with the high accuracy of 0.3 K required by climate studies. Biases in the SST product, arising when the factors that increase the optical path-length (absorbents concentration in the atmosphere or viewing angles) are large, cause problems in the use of the split-window formulation for climate monitoring. The reason is that the split-window coefficients currently used are not adequate to cover for all the atmospheric variability. To show this, simulations of channels 4 and 5 of AVHRR/2 of NOAA-11 using a radiative transfer model have been made. The range of atmospheric conditions and surface temperatures introduced in the simulation covers the variability of these parameters on a worldwide scale. From these data, the authors present new split-window coefficients that take into account the atmospheric variability through the ratio of the channel transmittances, or else through the total water vapor content along the path. They also show, using simulated and actual data, that the proposed split-window algorithm has a real global character and represents an improvement over the conventional algorithms     

基于OGRE的三维红外云仿真

根据实际需要简化了云的辐射模型,利用Modtran软件对整个场景的大气进行计算,得到大气路径辐射和透过率。再通过GPU的并行处理对整个数据进行采样,模拟出大气辐射传输效应。最后通过OGRE的粒子系统模拟出三维云体,结合云的自身辐射特性和大气效应模拟出三维红外云场景。仿真结果表明,生成的三维红外云场景有较好的真实感和实用性,为后续其它研究提供基本数据。     

Retrieval of cloud base heights from passive microwave and cloudtop temperature data

Water vapor profiling algorithms that treat liquid clouds explicitly yield a cloud base height as a byproduct. A single case of a water vapor profile retrieval using a combination of the SSM/T-2 on the DMSP satellite and cloud parameters from the AVHRR on the NOAA satellite retrieved a reasonable cloud base. While hardly definitive, this case is suggestive. The authors examine the cloud base signal in a combination of the SSM/1 and SSM/T-2 on the DMSP satellite from a theoretical point of view. It is shown that the signal is strong enough for a useful retrieval only over the ocean. For low altitudes, a cloud top temperature (CTT) constraint, as could be provided from an infrared radiometer, is required. While difficult with the DMSP-NOAA satellite combination, this has become much easier with the recent launch of NOAA-K with the AMSU-B and AVHRR. It is shown that the signal is acceptable over the relevant range of cloud liquid water content values. To achieve useful results, some local tuning of the algorithm will be necessary. This tuning could take the form of water vapor profile covariance matrices, climatological estimates of the cloud liquid water density, or purely empirical methods. Broken and multilayer clouds provide additional complications to the problem     

Atmospheric correction against algorithm for NOAA-AVHRR products:theory and application

Investigation of the effect of atmospheric constituents on NOAA Advanced Very High Resolution Radiometer (AVHRR) visible and near-infrared data is presented. The general remote sensing equation, including scattering, absorption, and bidirectional reflectance effects for the AVHRR solar bands, is described. The magnitude of the atmospheric effects for AVHRR solar bands with respect to their impact on the normalized difference vegetation index (NDVI) and the surface bidirection reflectance is examined. Possible approaches for acquiring atmospheric information are discussed, and examples of atmospheric correction of surface reflectance and NDVI are given. Invariant effects (ozone absorption and molecular scattering) and variant effects (water vapor absorption and aerosol scattering) are shown to dominate the atmospheric effects in the AVHRR solar bands     

Impact of variable atmospheric water vapor content on AVHRR datacorrections over land

This paper explores the impact of the integrated water vapor content (IWV) in the atmospheric column on the corrections of optical satellite data over land. First, simulation runs were used to quantify the trends in red and near infrared parts of the electromagnetic spectrum. Second, advanced very high resolution radiometer (AVHRR) measurements obtained over Canada during the 1996 growing season, together with reanalyzed IWV content data, were employed to determine the actual impact of constant IWV values. Third, various options in characterizing IWV for atmospheric corrections of AVHRR composites were examined. It was found that (1) as expected, IWV affects near-infrared radiation substantially more than red, although the latter is also altered; (2) that additional, subtle interactions take place between IWV, radiance levels, and viewing geometry that influence the retrieved surface reflectance; (3) that spatial and temporal variation in IWV caused changes in the normalized difference vegetation index up to 7.5% in relative terms during the peak green period; and (4) that IWV varies so substantially that pixel and date-specific values need to be used for the atmospheric correction of AVHRR data. At present, subdaily gridded IWV data sets from atmospheric data reanalysis projects are the only candidate source for such purpose     

基于神经网络的红外辐射大气透过率建模及计算

基于定点测量的标准黑体温度实验数据,建立大气透过率的神经网络估计模型。在不同距离测量黑体温度后,引入BP网络自适应学习测试数据的潜在规律,建立大气透过率与当前测量距离及测试温度之间的函数关系,可以精确计算目标的实际温度。仿真结果表明,用本文方法所建的BP网络可以有效地学习样本信息,建立的非线性大气透过率模型解决了大气透过率因影响因素复杂计算难度大等问题。     

Remote sensing of atmospheric water content from satellites using microwave radiometry

Analysis is presented which substantiates the high correlation achieved in relating integrated water vapor and liquid water to brightness temperatures at frequencies near the 22.235 GHz water vapor line. The influence of atmospheric and surface variability is shown to be minimal over low emissivity sea surfaces. Determination of atmospheric water content using regression techniques is shown to follow directly from radiation transfer theory. Satellite data from the Nimbus-E Microwave Spectrometer (NEMS) aboard Nimbus-5 are compared with radiosonde water vapor measurements and cloud images recorded by the Temperature Humidity Infrared Radiometer aboard Nimbus 5.     

Beam-filling effect correction with subpixel cloud fraction using a neural network

In this study, the effects of cloud inhomogeneity on microwave rain rate retrievals are investigated. A single-channel (85 GHz) empirically based algorithm using a neural network approach is presented. The objective is to correct the beam-filling error (BFE), that might occur because of the inherent variability within coarse microwave pixels, with subpixel information. To this aim, we used the Tropical Rainfall Measuring Mission passive microwave, thermal infrared and radar data. The integration of spatial information into the retrieval algorithm enables us to partially overcome the BFE. We use two parameters which characterize the horizontal cloud inhomogeneity within the microwave radiometer field of view, and we add them to simulated brightness temperatures as inputs of the neural network algorithm. The first one is the cloud fraction derived from infrared measurement, and the second corresponds to the fraction of the rainy area derived from radar measurements. The output rain rates were validated using the Precipitation Radar data. It was found that adding cloud fraction of microwave pixels, can lead to more accurate retrievals. Instantaneous precipitation estimates demonstrated correlations of /spl sim/0.6-0.7 and /spl sim/0.7-0.8 with radar-derived rain rates, for ocean and land retrievals respectively. In spite of the problem inherent in deriving the cloud (or rain) fraction, the initial validation results presented in this study are reasonably encouraging and show the advantage of utilizing the information from different sensors in order to optimize the retrieval of rainfall.     

Atmospheric corrections in the thermal infrared: global and watervapor dependent split-window algorithms-applications to ATSR and AVHRRdata

The split-window method is an appropriate way to perform atmospheric corrections of satellite brightness temperatures in order to retrieve the surface temperature. A climatological data set of 1761 different radio soundings, the TIGR database, has been used to develop two different split-window methods. A global quadratic (QUAD) method, with global coefficients to be applied on a worldwide scale, and a water vapor dependent (WVD) algorithm. The first method includes a quadratic term in the split-window equation that roughly accounts for the water vapor amount. The other method explicitly includes the water vapor amount in each split-window coefficient. When applied to the 1761 radio soundings, the latter method gives better results than the global one, especially when the surface emissivity is far from unity (0.95 or less) and when the water vapor reaches great values. Both algorithms have been tested on ATSR/ERSI and AVHRR/NOAA data over sea pixels. The QUAD algorithm gives correct results for simulations (the standard error is 0.2 K) and experimental data (the bias ranges from -0.1 to 0.4 K). The WVD algorithm appears to be more accurate for both simulations (the standard error is less than 0.1 K) and AVHRR experimental data when climatological water vapor contents are used (the bias ranges from -0.2 to 0.1 K)     

基于OpenFlow流表的云网络异常行为主动监测和处理

云数据中心的网络异常行为不仅对网络设备造成严重业务负荷,同时也显著影响云用户使用体验。云计算环境中的共享资源模式和云用户迥然不同的业务形态,使得网络分析和异常行为定位变得更加困难。本文针对云数据中心的网络异常行为进行特征提取和分析,并基于sdn云数据中心的网络架构和原理进行深度剖析,总结出基于openflow流表的网络异常行为判定方法。同时采用自动化运维手段,制定了一套网络异常行为自动化检测和封堵的智能系统,实现对网络异常行为的快速处理。     

一种红外目标模拟器的大气传输校准方法研究

红外辐射在大气中传输会在大气分子、气溶胶粒子的吸收和散射以及大气自身辐射的影响下发生变化,导致红外辐射测量精度的降低。为消除大气在红外目标模拟器校准中的影响,在基于恒定标准源的宽动态红外辐射测量方法的基础上,提出了一种红外目标模拟器的大气传输校准方法。在水平均匀大气近距离的红外目标模拟器校准中,利用卷积神经网络的数据分析能力建立了不同波段、不同温度、不同距离下的大气透过率和大气程辐射的动态模型,将探测器输出电压作为基于编码器-解码器结构的卷积神经网络的输入,按照训练流程对网络进行训练,在实验环境下预测了大气传输对红外辐射的影响。所建模型能够反映大气透过率和大气程辐射的动态变化规律,并通过红外辐射反演对提出的方法进行了验证。实验结果表明：基于编码器-解码器结构的卷积神经网络算法能够较好地预测大气透过率和大气程辐射,在三个波段下的平均误差为3.078 3%、3.818 6%、5.345 2%,低于传统方法,降低了大气透过率和大气程辐射的影响,从而减小了红外辐射的测量误差,提高了校准精度。     

Estimating leaf area index from satellite data

A method for estimating leaf area index from visible and near infrared measurements of vegetation above a soil background is applied to a Landsat Thematic Mapper data set. Some constants required for the procedure are inferred from the scattergram of data values. The resulting image illustrates variability of leaf area index over an agricultural area. The mixed-pixel case, corresponding to low-resolution data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) is also discussed, and a vegetation index is suggested for both high- and low-resolution data. Consideration of the two types of data leads to the suggestion that a sampled high spatial resolution sensor (50-100 m) be added to the AVHRR in order to permit accurate inference of vegetation conditions over agricultural areas     

Early Warning and Crop Condition Assessment Research

The Early Warning Crop Condition Assessment Project of AgRISTARS was a multiagency and multidisciplinary effort. Its mission and objectives were centered around development and testing of remote-sensing techniques that enhance operational methodologies for global crop-condition assessments. The project developed crop stress indicator models that provide data filter and alert capabilities for monitoring global agricultural conditions. The project developed a technique for using NOAA-n satellite advanced very-high-resolution radiometer (AVHRR) data for operational crop-condition assessments. This technology was transferred to the Foreign Agricultural Service of the USDA. The project developed a U. S. Great Plains data base that contains various meteorological parameters and vegetative index numbers (VIN) derived from AVHRR satellite data. It developed cloud screening techniques and scan angle correction models for AVHRR data. It also developed technology for using remotely acquired thermal data for crop water stress indicator modeling. The project provided basic technology including spectral characteristics of soils, water, stressed and nonstressed crop and range vegetation, solar zenith angle, and atmospheric and canopy structure effects.     

不同波长近红外激光大气消光特性的泛化回归神经网络反演算法

由于大气的不均匀性和不稳定性,采用MODTRAN等大气传输软件计算,很难保证实时精确地获取某波段的近红外激光大气消光系数、大气透过率等消光特性,且传统的手段存在很多不确定性因素.利用近红外波段激光不同波长大气消光特性之间的相关性和泛化回归神经网络,可以实现输入到输出之间非线性的相关性函数关系,通过已知波长的激光雷达实测数据来实时反演其他波长的大气消光特性.实践表明,该方法为实时获取实战条件下某一波段近红外大气消光特性提供一种新方法.     

红外化学遥感数字信号处理算法的研究进展

介绍了几种为远距离红外蒸汽信号监测开发的分类器设计，频域数字滤波，时域数字滤波等数字信号处理技术。对线性分类器，分段线性分类器和神经网络分类器等在红外化学遥感系统中的应用做了初步的探讨。对背景扣除技术，频域数字滤波技术，数字滤波的切趾方法等做了简要的描述。阐述了时域滤波技术的基本原理及实际应用。展望了红外化学遥感数字信号处理算法的研究前景。     

基于二值神经网络的大场景点云分类

近年随着3维数据采集技术不断发展,大场景 点云数据的获取越来越方便。目前深 度学习网络框架在2维图像处理领 域越来越成熟,而大场景点云是一种3维无规则化的数据,3维卷积神经网络直接处理大场 景3维数据会存在分类精度低和计 算复杂等问题。因此为了有效解决基于深度学习的点云分类任务中存在的计算时间长和分类 精度低的问题,本文提出基于二值 神经网络的大场景点云分类方法,针对不规则的3维点云数据设计特征值计算方法,基于IR -Net二值神经网络处理输入的点云 特征图像,进一步采用Dynamic ReLU激活函数,提高神经网络的计算效率,最后得出点云分 类结果。实验结果表明,所提出 的方法在Oakland数据集上分类精度达到97.6%,在GML数据集中取得 了92.3%和97.2%的分类精度,实验结果证明Dy -ResNet 能够有效提升了点云分类的精度,减少计算的复杂度,并提高了训练效率。     

A procedure for the detection and removal of cloud shadow fromAVHRR data over land

Although the accurate detection of cloud shadow in AVHRR scenes is important for many atmospheric and terrestrial applications, relatively little work in this area has appeared in the literature. This paper presents a new multispectral algorithm for cloud shadow detection and removal in daytime AVHRR scenes over land. It uses a combination of geometric and optical constraints, derived from the pixel-by-pixel cross-track geometry of the scene and image analysis methods to detect cloud shadow. The procedure works well in tropical and midlatitude regions under varying atmospheric conditions (wet-dry) and with different types of terrain. Results also show that underdetected cloud shadow ran produce errors of 30-40% in observed reflectances for affected pixels. Moreover, radiative transfer calculations show that the effects of cloud shadow are comparable to or exceed those of aerosol contamination for affected pixels. The procedure is computationally efficient and hence could be used to produce improved weather forecast, land cover, and land analysis products. The method is not intended for use under conditions of poor solar illumination and/or poor viewing geometry     

道路三维点云多特征卷积神经网络语义分割方法

针对道路场景下三维激光点云语义分割精度低的问题,提出了一种基于卷积神经网络并结合几何点云多特征的端到端的语义分割方法。首先,通过球面投影构造出点云距离、相邻夹角及表面曲率等特征图像,以便于应用卷积神经网络;接着,利用卷积神经网络对多特征图像进行语义分割,得到像素级的分割结果。所提方法将传统点云特征融入到卷积神经网络中,提升了语义分割效果。使用KITTI点云数据集进行测试,结果表明:所提三维点云多特征卷积神经网络语义分割方法的效果优于SqueezeSeg V2等没有结合点云特征的语义分割方法;与SqueezeSeg V2网络相比,所提方法对车辆、自行车和行人分割的精确率分别提高了0.3、21.4、14.5个百分点。     

基于BP神经网络的红外透过率计算

针对实时条件下中红外波段平均大气透过率的计 算,提出了一种基于贝叶斯正则化BP神经网络的方法。 利用BP神经网络良好的非线性拟合特点,建立大气参数与中红外平均透过率之间的关系 模型,从而可以准确迅速 地得到计算结果。此网络模型是以实测温度、压强、湿度和气溶胶的后向散射系数作为输入 向量,分别以水蒸气和CO₂吸收透过率、气溶胶散射透过率和大气透过率作为输出。仿 真结果表明:在相同的大气参数下,本方法的计算 结果与期望值之间的相对误差较小,且远小于经验公式法,验证了本方法的可行性与有效性 。因此,本方法对大气透过率的准确地快捷计算提供了有益的借鉴。