Aerosol retrieval over land from AVHRR data-application foratmospheric correction

Correction of Advanced Very High Resolution Radiometer (AVHRR) imagery for the aerosol effect requires retrieval of the aerosol loading from the images. Two retrieval algorithms that were previously developed for Landsat are modified for the AVHRR. The methods determine the aerosol optical thickness over land surfaces from AVHRR band one data independently of ancillary information. The first method retrieves aerosols based on the atmospheric effect on the path radiance. This method requires the surface reflectance to be 0.02±0.01, which is found over forests in the red channel. Two techniques are used to screen an AVHRR scene for pixels that have this low reflectance. The qualifying requirements for these techniques are discussed, and the method is demonstrated to retrieve aerosol optical thicknesses to ~±0.1. The second method uses the change in contrast for several scenes to determine the change in the optical thickness between the scenes. A reference scene allows absolute determination. The method has an rms error of ~0.1     

An automated, dynamic threshold cloud-masking algorithm for daytime AVHRR images over land

An operational scheme for masking cloud-contaminated pixels in Advanced Very High Resolution Radiometer (AVHRR) daytime data over land is developed, evaluated, and presented. Dynamic thresholding is used with channel 1 reflectance data, channel 3 minus channel 4 temperature difference data, and channel 4 minus channel 5 temperature difference data to automatically create a cloud mask for a single image. The dynamic thresholds can be applied in two different ways: to each pixel individually and to classes of pixels determined by an unsupervised minimum Euclidian distance classifier. The dynamic threshold cloud-masking (DTCM) algorithm presented in this study is used to produce cloud masks based on three different configurations: two channels and individual pixels, three channels and individual pixels, and three channels and classes of pixels. These cloud masks are compared with control masks that were created by visual inspection. The results from the clouds from AVHRR (CLAVR) algorithm and the cloud and surface parameter retrieval (CASPR) algorithm are also compared with the control masks. The results of the comparisons indicate that DTCM, applied on a pixel-by-pixel basis, correctly identifies more clear pixels than CASPR or CLAVR while correctly identifying a comparable or higher number of cloud-contaminated pixels.     

国产卫星多角度偏振传感器的光谱特征云检测方法研究

在卫星遥感研究中, 云检测是基础环节, 其结果影响大气、地表各种参数的定量遥感, 同时也影响云微物理特 性的反演。本研究针对多角度偏振卫星载荷(高分五号DPC 传感器), 建立了一种改进的光谱特征云检测算法。该算 法综合利用云像元和非云像元在可见光反射率光谱、氧A 波段吸收、蓝光偏振反射率以及偏振虹等特性上的差异, 分别提出了陆地、海洋上空的云检测方案, 并进一步建立了多角度云检测融合策略以标记云、晴空和未定像元。在 陆地检测中, 通过增加表观压强检测和偏振虹检测分别改进了高层薄云和低层薄云的识别; 在海洋检测中, 利用表观 压强与云层的退偏特性改进了耀光区云像元的识别。全球云检测结果示例显示该算法整体检测效果较好, 同时典型 区域的检测结果与MODIS 云产品也具有较好的一致性。该研究可为高分五号02 星上的多角度偏振传感器云检测提 供方法基础。     

Application of neural networks to AVHRR cloud segmentation

The application of neural networks to cloud screening of AVHRR data over the ocean is investigated. Two approaches are considered, interactive cloud screening and automated cloud screening. In interactive cloud screening a neural network is trained on a set of data points which are interactively selected from the image to be screened. Because the data variability is limited within a single image, a very simple neural network topology is sufficient to generate an effective cloud screen. Consequently, network training is very quick and only a few training samples are required. In automated cloud screening, where a general network is designed to handle all images, the data variability can be significant and the resulting neural network topology is more complex. The latitudinal, seasonal and spatial dependence of cloud screening large AVHRR data sets is studied using an extensive data set spanning 7 years. A neural network and associated feature set are designed to cloud screen this data set. The sensitivity of the thermal infrared bands to high atmospheric water vapor concentration was found to limit the accuracy of cloud screening methods which rely solely on data from these channels. These limitations are removed when the visible channel data is used in combination with the thermal infrared data. A post processing algorithm is developed to improve the cloud screening results of the network in the presence of high atmospheric water vapor concentration. Post processing also is effective in identifying pixels contaminated by subpixel clouds and/or amplifier hysteresis effects at cloud-ocean boundaries. The neural network, when combined with the post processing algorithm, produces accurate cloud screens for the large, regionally distributed AVHRR data set     

Detection and removal of cloud contamination from AVHRR images

Describes the characteristics and performance of an algorithm designed to remove the effects of cloud contamination from AVHRR composite images over land. Using information in the temporal NDVI profile, the algorithm detects cloud-contaminated pixels, and optionally replaces these with interpolated values of individual AVHRR channels or channel transformations. As tested, the algorithm detects only those effects of residual clouds that result in a temporary decrease of the NDVI. It was found that the algorithm is capable of identifying clouds of varying opacity as well as cloud shadows present in the composites, and that its use appears preferable to compositing over longer periods. The algorithm can be applied without the need for ancillary information, e.g., on expected surface radiance conditions. Although developed for use in biospheric studies in Canada, it could also be applied at other latitudes where the seasonal trajectories of satellite-derived variables exhibit a single maximum or, by extension, a single minimum     

An optimum interpolation method applied to the resampling of NOAAAVHRR data

Two main problems must be solved in the geometric processing of satellite data: geometric registration and resampling. When the data must be geometrically registered over a reference map, and particularly when the output pixel size is not the same as the original pixel size, the quality of the resampling can determine the quality of the output, not only in the visual appearance of the image, but also in the numerically interpolated values when used in multitemporal or multisensor studies. The “optimum” interpolation algorithm for AVHRR data is defined over a 6×6 window in order to: consider overlapping effects among adjacent pixels. The response for each new pixel R(x, y) is determined as a linear combination of the response R _i(x_iy_i) of the surrounding pixels in the window (i=1,36). The weighting coefficients μ_i are calculated from the ground projection of the effective spatial response function for each AVHRR pixel, taking into account the particular viewing angle and geometry of the pixels on the ground. This method is intended to give an optimal interpolation of AVHRR scenes along all the scanline, in order to compensate for off-nadir radiometric alterations associated to the varying spatial resolution and the blurring introduced by the pixel overlaps. The optimum method, as mathematically defined, is highly expensive in CPU time. Then, a big effort is necessary to implement the algorithms so that they could be operationally applied. Two approaches are considered: a general numerical method and a pseudo-analytical approximation. A Landsat TM image corresponding to the same date of the AVHRR image is used to test the quality of the radiometric interpolation procedure     

相干激光引信在云雾干扰下的回波特性研究

为分析相干激光引信在云雾干扰下的探测性能,基于Mie散射理论和Monte Carlo方法建立了相干激光引信在云雾中的探测模型,仿真获取相干激光引信在多种云雾干扰场景下的探测回波,分析回波信号的时域和频域特性,以及云雾能见度、探测位置和探测角度对回波特性的影响。研究结果表明,相干激光引信可利用回波频域特征准确探测和识别目标,且不易受云雾能见度、探测位置和探测角度等因素的影响。结果证明:相干激光引信在云雾干扰下具有优良的探测性能。     

Land cover dependence in the detection of contaminated pixels insatellite optical data

The detection of partially contaminated pixels over land is necessary for quantitative applications of satellite optical measurements to estimate surface biophysical parameters such as leaf area index or vegetation composition. Threshold-based algorithms suffer from the heterogeneity of land cover and the seasonal variability of the radiation reflected and emitted by the land surface. As an alternative, a method based on a Fourier series approximation to the seasonal trajectory of the normalized difference vegetation index (NDVI) had been previously developed (Cihlar 1996). In this paper, we introduce modifications to the basic algorithm to more closely represent NDVI seasonal trends for different land cover types, as well as a simplified way to determine the time- and pixel-specific contamination thresholds. Based on the tests with 1993-1996 Advanced Very High Resolution Radiometer (AVHRR) data over Canada, the modified procedure effectively detects contaminated pixels for boreal ecosystems after the growing season of interest. The modifications also improved its performance while the growing season is in progress; in this case, at least one complete previous growing season coverage is required to provide the temporal series needed to establish the thresholds. The modified procedure also yields a contamination parameter that may be used to estimate the most likely value for NDVI or other variables for each pixel. It is concluded that the procedure would perform effectively in other areas, provided that the NDVI temporal trajectories of the cover types of interest can he represented by a mathematical model     

Image-Based Traffic Monitoring With Shadow Suppression

For a vision-based traffic monitoring and enforcement system, shadows of moving objects often cause serious errors in image analysis due to misclassification of shadows and moving vehicles. An effective shadow suppression method is thus required to improve the accuracy of image analysis and this paper proposes a novel color-space ratio model for detecting shadow pixels in traffic imagery. The proposed approach does not require many image sequences for constructing the model. Instead the model can be easily built up using a shadow region in a single image frame. To increase the accuracy of shadow detection, we design two types of spatial analysis to verify actual shadow pixels. Comparative results show that the proposed method works better than several well-known methods. The proposed methods have been applied to an image-based traffic monitoring system for detecting shadow pixels in traffic imagery. The experimental results not only validate the feasibility of the proposed algorithm but also successfully estimate traffic parameters such as traffic flows, traffic densities, vehicle turn ratios and vehicle speeds, all with satisfactory accuracy     

探测气溶胶-水汽的拉曼-米散射激光雷达系统

设计和构建了发射波长为355 nm和532 nm的户外型全天时激光雷达系统,用于探测大气气溶胶和水汽。运用355 nm和532 nm的米散射、532 nm的偏振、氮气和水汽分子的拉曼激光雷达技术,用于对边界层结构、对流层气溶胶和云光学特性及其形态、水汽混合比进行连续探测研究。该系统结构紧凑,运输方便,具备远程操作、数据传输、一键式启动等功能。利用该系统对大气气溶胶和水汽进行探测,探测结果表明：在大气气溶胶的探测过程中,在重污染条件下混合层高度较干净天低,在0.5 km以下,而干净天在1 km左右;通过对消光系数、Angstrom指数和退偏振比分析可知,重污染条件下,底层大气气溶胶以球形粗粒子污染物为主,干净天底层大气气溶胶以球形细粒子污染物为主;在云层中,Angstrom指数明显减小,且出现负值,说明云粒子半径较大。在水汽探测过程中,采用自标定方法获得系统的标定常数为121,与已标定的激光雷达系统对比,误差在±0.3 g/kg以内;连续探测结果表明可对夜晚5 km及白天混合层以内进行探测。该系统满足产品化的需求,可广泛运用于大气环境的监测领域中。     

Satellite imagery based adaptive background models and shadow suppression

Accurate segmentation of foreground objects in video scenes is critical for assuring reliable performance of vision systems for object tracking and situational awareness in outdoor scenes. Most existing techniques for background modeling and shadow suppression require that a number of parameters be “hand-tuned” based on environmental conditions. This paper presents two contributions to overcome such limitations. First, we develop and demonstrate a satellite imagery based approach for selecting appropriate background and shadow models. It is shown that the illumination conditions (i.e. cloud cover) of a scene can be reliably inferred from visible satellite images in the local region of the camera. The second contribution presented in the paper is introduction and evaluation of a Hybrid Cone-Cylinder Codebook (HC3) model which combines an adaptive efficient background model with HSV-color space shadow suppression into a single coherent framework. The structure of the HC3 model allows for seamless fusion of the satellite data. We are thereby able to exploit the fact that, for example, shadows are more pronounced on sunny days than cloudy days, allowing for more sensitive detection. The paper presents a set of experiments using day long sequences of videos from an operational surveillance system testbed. Results of these experimental analyses quantitatively illustrate the benefits of using satellite imagery to inform and adaptively adjust background and shadow modeling.     

改进的自适应灰度视频序列阴影检测方法

提出了一种自适应的阴影检测方法,去除了传统固定阈值阴影检测方法残留的阴影边缘,有效地改善了阴影检测效果。首先采用kmeans聚类、求前景灰度直方图峰值间平均值等方法得到自适应的阈值,在此基础上,计算满足阈值约束的前景像素点,将该点及其8邻域点都作为可去除的阴影点进行标记。最后,去除标记的阴影点及极小面积的前景区域。本文对已有的阴影检测算法进行了改进,加入了自适应的阈值计算方法并去除了原有算法检测后残留的阴影边缘,在对室内及室外视频序列进行的检测中都取得了较好的效果。      

基于二维道路图像理解算法的阴影检测

提出了一种新的自动检测二维彩色道路场景图像阴影区域的方法。该方法首先从RGB色彩空间中提取出亮度信息,并进行图像分割,再结合从色差图像中提取的阴影信息,检测并消除可能存在的阴影区域,减小在直方图分割中由于阴影的影响而引起的分割误差,最终达到正确的图像分割结果。实验结果表明,该方法简便且有效,并能适应各种不同的二维道路场景需求,具有一定的鲁棒性。     

基于深度帧差卷积神经网络的运动目标检测方法研究

复杂场景中的运动目标检测是计算机视觉领域的重要问题,其检测准确度仍然是一大挑战.本文提出并设计了一种用于复杂场景中运动目标检测的深度帧差卷积神经网络（Deep Difference Convolutional Neural Network,DFDCNN）.DFDCNN由DifferenceNet和AppearanceNet组成,不需要后处理就可以预测分割前景像素.DifferenceNet具有孪生Encoder-Decoder结构,用于学习两个连续帧之间的变化,从输入（t帧和t+1帧）中获取时序信息;AppearanceNet用于从输入（t帧）中提取空间信息,并与时序信息融合;同时,通过多尺度特征图融合和逐步上采样来保留多尺度空间信息,以提高网络对小目标的敏感性.在公开标准数据集CDnet2014和I2R上的实验结果表明：DFDCNN不仅在动态背景、光照变化和阴影存在的复杂场景中具有更好的检测性能,而且在小目标存在的场景中也具有较好的检测效果.     

激光测高卫星大气散射延迟改正现状及展望

激光测高卫星可以在大范围内获得亚米甚至厘米级的地表高程信息,但不可避免受云、气溶胶等粒子引起的散射影响,其中前向散射引起的激光测距和最终测高误差不容忽视。文中系统梳理激光测高卫星大气散射误差改正技术,介绍了国内外卫星激光测高系统参数、大气探测及散射改正算法,有别于已有的蒙特卡洛模拟改正方法,提出了一种基于指数函数模型的大气散射改正算法,选择青海湖等区域的ICESat/GLAS开展试验,结果表明,光学厚度小于2时能有效提升受大气散射影响的数据精度,同时提升数据可利用率约9%,该算法更易于实现业务化应用。最后针对大气参数同步探测的必要性,结合星载大气参数探测设备对后续国产激光测高卫星大气散射改正提出了若干建议。     

Cloud fraction and cloud shadow property retrievals fromcoregistered TIMS and AVIRIS imagery: the use of cloud morphology forregistration

The Thermal Infrared Multispectral Scanner (TIMS) and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) were operated simultaneously from the ER2 aircraft during a March 1990 test over the Rio Bravo region, Belize. Coregistration of the imagery obtained by these two instruments is necessary to utilize the data effectively. A technique for registering the TIMS imagery to AVIRIS imagery is presented. It takes advantage of the morphology of the fair weather cumulus (FWC) clouds present in the imagery for estimating inter-sensor distortions. It relies on an iterative process in which skew, nearest neighbor sampling, and cross-correlation (1D and 2D) are applied. Comparison between the AVIRIS three-band ratio (3BR) imagery and the coregistered TIMS imagery shows that TIMS is superior in detecting thin cloud and cloud edge pixels, especially over shadowed background. Although the seven scenes analyzed in the study were obtained within the same one-hour time period and over the same geographical region, the optimum temperature threshold for cloud detection, with respect to the background temperature, varies significantly from 2.1 to 3.3°C. These values agree with the AVIRIS 3BR cloud fraction equivalent temperature thresholds to within 0.5°C. When applying a cloud shadow mask from the AVIRIS near infrared imagery to the coregistered TIMS background imagery, a 1°C temperature differential is found between the shadowed and nonshadowed background. This significant radiative cooling by Fair Weather Cumulus cloud shadows could introduce errors in surface emissivity retrievals by other Earth Observing System (EOS) investigators     

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     

The GLOBCARBON Cloud Detection System for the Along-Track Scanning Radiometer (ATSR) Sensor Series

The GLOBCARBON initiative aims to develop a service to generate fully calibrated estimates of at-land products using, in combination, data from the Along-Track Scanning Radiometer (ATSR) sensor series, MERIS and VEGETATION. A fundamental requirement of such processing is the effective removal of extraneous effects introduced by cloud, cloud shadow, snow, and atmosphere in these data. This paper reports on the implementation and extension of a new cloud and snow processing system for land, which is for the ATSR sensor series. The snow processor is as implemented for the Moderate Resolution Imaging Spectrometer, whereas the cloud processor is based on APOLLO, with extensions to generate the probabilities of cloud presence and a further test to eliminate the cloud missed by APOLLO. The system was tested on 48 images representing a wide range of cloud types and forms over three contrasting biomes-boreal forest, desert savannah, and tropical evergreen forest. Excellent results are produced and represent a major improvement over the original flagging system. The approach has been adopted in GLOBCARBON for the processing of 500 000 ATSR scenes and 20 000 AATSR striplines.     

Bidirectional NDVI and atmospherically resistant BRDF inversion for vegetation canopy

The normalized difference vegetation index (NDVI) has been widely applied in optical remote sensing. However, it has been demonstrated that NDVI is still partially affected by atmospheric path scattering and bidirectional (illumination and viewing geometry) effects. In this paper we present the benefit of using a bidirectional NDVI, and we discuss the problems in using the maximum NDVI composite method. Based on the assumption that a clear day has a larger NDVI value and a cloudy day has a smaller NDVI value (smaller reflectance in the near-infrared band and larger reflectance in red band due to atmospheric path scattering), the ratio of squared observed NDVI values and calculated NDVI values is used as a weight in our inversion method. The calculated NDVI values are derived from previously inverted bidirectional reflectance distribution functions (BRDFs). The inversion process will loop until all weights converge. Our research on the early Terra/MODIS data using a semiempirical kernel-driven BRDF model (the RossThick-LiTransit model) shows that this new method can improve inversion results whenever some cloudy pixels are not filtered out. As cloud detection and subpixel cloudiness are always a problem, this technique should still be very useful in improving the quality of BRDF inversion.     

Voting-based separation of diffuse and specular pixels

A fast and efficient algorithm to separate diffuse and specular pixels in a single image without explicit colour segmentation or any prior information is proposed. It is applicable to highly textured objects/scenes under arbitrary illumination conditions. Experimental results show accurate separation results for natural images.