基于SBDART辐射传输模型的夜间辐射雾自动检测及时间序列分析

如何确定合适的阈值来区分夜间辐射雾、晴空地表和中高云一直是雾检测研究的重点。圣巴巴拉DISORT大气辐射传输模型(Santa Barbara DISORT atmospheric radiative transfer,SBDART)可模拟雾顶亮度温度。基于该模型获取MODIS B20与B31波段的亮温差(brightness temperature difference,BTD),将其用于夜间辐射雾检测。以MODIS卫星数据为可行性试验数据,用国家卫星气象中心提供的地面验证数据进行验证,结果表明,使用该模型监测夜间雾的准确率达78.3%,误判率为21.7%,可靠性指标为0.643,Kappa系数为0.730。为进一步验证方法的稳定性,选取8景卫星序列图像进行时间序列分析,结果显示Kappa系数均值为0.744,说明应用当前阈值方法对MODIS夜间雾检测具有可适用性。该方法为夜间雾预报和夜间雾参数反演提供了有效的参考。     

低云、高云和白天陆地雾遥感分离研究

雾灾一直都在影响人们日常生活,对雾进行有效的监测可减轻雾灾的危害程度.目前大雾遥感检测的难点为低云和雾的分离,并且高云下面的雾经常被误分掉,因此本文结合现有的研究成果和基于Gabor变换的纹理特征来分离低云和雾,并对高云和雾在透光性和光谱特性进行了研究,用红波段与近红外波段构建的归一化雾指数来提取高云下面的雾区.使用中...     

基于DNB验证的VIIRS夜间云检测方法

针对夜间云检测验证中低云和雾难以区分的困难,提出了对于南方山区有效的云检测和验证方案。通过分析可见光红外成像辐射仪套件(visible infrared imager radiometer suite,VIIRS)传感器数据的新特性和云检测的原理,给出了适合VIIRS夜间云检测的方法。对白天/夜间波段(day and night band,DNB)数据对云检测验证的适用性进行了分析。结果表明:在月亮天顶角小于60°时,DNB波段能够较好地用于夜间云检测验证;在扫描角小于15°时,云检测精度不低于91%;使用VIIRS的M12和M13通道的亮温差值BTM12-BTM13辅助M12和M15通道的亮温差值BTM12-BTM15进行低云检测,能够去除大部分山谷中雾的影响;检测阈值对扫描角大小变化敏感,当扫描角较大时,设定的阈值在检测精度上不如扫描角较小时理想。     

与地震有关的活动断裂带红外辐射变化特征

为了获得与地震有关的活动断裂带红外辐射变化信息,以2010年4月14日青海省玉树Ms7.1级地震为研究对象,利用MODIS热红外波段遥感数据研究了地震前后发震构造甘孜—玉树断裂带的亮温演化特征,分析了地震发生当月每10 d亮温变化情况,并与2011年同期该断裂带的亮温特征进行对比.结果表明:地震前一个月内,断裂带附近亮温值偏高,亮温变化最为显著的区域出现在断裂带北端,与野外考察获得的本次地震地表破裂带位置相吻合;震后7～16d,断裂带附近出现了明显的亮温增高现象,结合地震发生时水汽及CO变化情况,认为这一亮温增高现象与震后地下气体的大量释放有关;2011年5月断裂带附近的亮温高值可能与同年6月发生的Ms5.3级囊谦地震有关.因此认为,断裂带亮温的显著变化对断裂活动有一定指示作用,利用热红外遥感数据研究主要断裂带红外辐射特征,对地震构造带活动性监测具有重要意义.     

面向卫星云图云分类的自适应模糊支持向量机

云类识别是实现卫星云图自动分析的基础,针对卫星云图易受噪声干扰且不同云系往往相互交叠的特点,构造一种面向云类识别的自适应模糊支持向量机。该方法不仅改进了隶属度函数的表现形式,而且通过定义控制临界隶属度和隶属度衰减趋势的参数,使隶属度能根据不同云系样本的具体分布特性自适应调整,解决了传统模糊支持向量机的隶属度函数难以反映样本分布的问题。在MTSAT卫星云图上的实验结果表明,通过提取云图可见光通道的反照率、红外通道的亮温及三种亮温差作为云图的光谱特征,并结合统计纹理特征,所构造的自适应模糊支持向量机分类器能有效区分晴空区、低云、中云、高云及直展云;云类识别准确率优于标准支持向量机和传统模糊支持向量机,且具有更强的稳定性和自适应性。     

利用MODIS数据监测夜间雾

在分析雾、云、地表、水体等目标物红外辐射特性的基础上,针对MODIS数据提出了夜间雾监测算法.试验表明,该算法能够将低云大雾与中高云、水体和地表区分开.     

祁连山和首都圈卫星热红外背景场变化特征

掌握非震情况下的热红外亮温背景场及其时空变化规律是有效提取地震红外异常信息的关键.利用2003-2011年NOAA卫星夜间热红外遥感数据构建祁连山和首都圈亮温背景场,分析其时空演化特征.结果表明:红外亮温背景场受季节、地形和断裂活动等多种因素的影响,其中受季节变化影响最大,年变规律明显;不同地理环境,亮温年变特征呈现不同形式,对于地形地貌复杂的地区,亮温变化曲线不稳定,红外亮温与地面高程呈显著的负相关关系,地面高程每增加100 m,亮温降低约0.21 ～ 0.63℃,这与我国气温直减率基本一致;活动断裂带在红外图像上表现为明显的高亮温线性条带或亮温分界带;多年平均背景场平滑了气候等突变信息,呈现出稳定性较强的规律性变化特征,为断裂活动和地震所引起的增温异常检测提供了稳定的亮温变化基准场.     

基于多光谱综合的MODIS数据云检测研究

云检测是卫星遥感数据处理中不可缺少的工作。通过分析云在不同波段中的大气辐射特点，结合MODIS数据的光谱特性，提出 一种多光谱综合的云检测方法。该算法从可见光反射率、红外波段亮温值以及亮温差等方面综合考虑，逐步建立一个云检测掩模。通 过对不同时期不同背景的MODIS数据进行验证和对比分析，结果表明，该模型的云检测效果理想，尤其对可见光波段难以识别的薄卷 云也有很好效果，为有效利用MODIS数据以及进行更加精确的反演提供可靠依据。     

基于FY-2E卫星数据的福建沿海海雾遥感监测

利用我国自主研制的风云二号静止气象卫星资料,结合地面自动气象站能见度资料,通过对大量不同时相卫星资料的试验分析,找出台湾海峡海雾、云以及晴空海表等典型下垫面的可见光、热红外和中红外3个通道的光谱特征和变化规律,在此基础上运用反射率阈值实现云雾与海洋表面的自动分离,用亮温阈值实现海雾和低云与中高云的自动分离,用中红外和热红外通道的归一化差值指标实现夜间海雾的自动识别,最后建立台湾海峡海雾自动监测业务软件系统,并选择2015年和2016年地面实测资料对遥感监测结果进行精度验证。研究结果表明:风云静止卫星逐时海雾产品能有效地弥补极轨卫星在监测时次上的不足,很好地为台湾海峡海雾动态监测业务提供数据支持;遥感监测结果与地面观测结果相吻合,总体上较为理想,海雾监测平均准确率白天超过70%,但夜间判识精度低于白天;但同时风云静止卫星遥感技术在海雾和低云的有效分离方面仍然存在局限性。     

基于VIIRS影像的秸秆焚烧火点监测方法

利用VIIRS SDR数据,基于火点像元亮温特征及其与背景亮温的偏差来提取秸秆焚烧火点信息。采用湖北省2015年秋收季节的VIIRS SDR 750 m分辨率下的第5、7、11波段反射率产品和13、15、16波段亮温值产品,基于VIIRS卫星影像秸秆焚烧火点监测算法和GIS平台叠加火点信息和地理基础数据生成湖北省秸秆焚烧火点分布图。结果表明,小火点检测率较高,漏检率降低。     

Himawari-8静止气象卫星时序法火点探测

随着新一代静止气象卫星的发射,高频次和高时效的观测特性对于火点探测具有独特优势。本文基于Himawari-8新一代静止气象卫星高频次观测特点,提出有利于火情初期火点判识的时序探测方法。与传统的极轨气象卫星遥感火情监测采用的上下文法不同,时序探测法判识火点的方法依据为探测像元亮温在观测时间上的差异。研究结果显示,在无云及无异常热源条件下,相邻时次中红外亮温差异较小,当前后时次亮温差达到3K时,可判识出火点,而上下文法的阈值均在6 K以上,时序法的火点判识阈值较上下文法明显降低,探测相应的亚像元火点面积减小一倍以上,从而提高了火情判识的灵敏度,实现火点早期发现。本文介绍了时序法火点判识方法,并以黑龙江桦川县的星地同步观测实验进行验证,研究表明,时序法较上下文法在初发火点探测灵敏度方面有明显优势,时序法和上下文法的结合可提高气象卫星对火情发展过程的监测能力。     

Monitoring fog using FY-1D meteorological satellite

FY-1D is the second national operation meteorological satellite of China, and is much better compared to monitoring fog. However, research on monitoring fog using FY-1D is very few. In this paper, based on the typical FY-1D data, a fog’s spectral characteristics in the different channels are analyzed using the histogram analysis method, and a method of monitoring fog using FY-1D is suggested. The results indicate that the 1st and 4th channels are the representative channels of FY-1D for the identification of fog. In the 1st channel, the fog is with uniform veins, smooth top, and clear-cut boundary, and its albedo is 20%～48%. In the 4th channel, the fog’s brightness and temperature is 272～289K, and the difference value between the fog’s and the ground surface’s is not more than 6K.     

A stochastic model for active fire detection using the thermal bands of MODIS data

Active fire detection using satellite thermal sensors usually involves thresholding the detected brightness temperature in several bands. Most frequently used features for fire detection are the brightness temperature in the 4-/spl mu/m wavelength band (T/sub 4/) and the brightness temperature difference between 4- and 11-/spl mu/m bands (/spl Delta/T=T/sub 4/-T/sub 11/). In this letter, the task of active fire detection is examined in the context of a stochastic model for target detection. The proposed fire detection method consists of applying a decorrelation transform in the (T/sub 4/,/spl Delta/T) space. Probability density functions for the fire and background pixels are then computed in the transformed variable space using simulated Moderate Resolution Imaging Spectroradiometer (MODIS) thermal data under different atmospheric humidity conditions and for cases of flaming and smoldering fires. The Pareto curve for each detection case is constructed. Optimal thresholds are derived by minimizing a cost function, which is a weighted sum of the omission and commission errors. The method has also been tested on a MODIS reference dataset validated using high-resolution SPOT images. The results show that the detection errors are comparable with the expected values, and the proposed method performs slightly better than the standard MODIS absolute detection method in terms of the lower cost function.     

顾及邻近关系的NPP/VIIRS序列影像时空插值方法

针对可见红外成像辐射仪(visible infrared imaging radiometer suite,VIIRS)月度夜光遥感影像的数据缺失问题,提出一种利用地物邻近关系相关性的像元时空插值方法,以时、空关系互相作为约束条件,将时序变化一致性较好的像元数据作为空间插值的参考,将空间关系一致性较好的月度数据作为时序插值的参考,通过构建不同的卷积核, 在时序和空间维度分别对初步插值结果进行卷积运算,求得待插值像元的时空插值。以2015年江苏省月度夜光遥感影像修复为例,对不同维度时空插值方法进行对比分析,结果表明, 空间维度插值虽然顾及到像元的空间关联性,仍无法满足数据大范围缺失的插值要求,插值结果整体偏低;时间维度插值考虑到像元的时间趋势性,插值精度较空间维度插值有一定提高,但部分月份插值结果有较大偏差;相对于三次Hermit插值,时空插值方法获得的月度影像灯光亮度总和的最大相对误差、年度影像灯光亮度总和相对误差以及逐像元差值均显著降低。总的来看,所提时空插值方法在插值过程中同时顾及到VIIRS数据的时间趋势平稳性和空间结构稳定性,影像插值精度提高明显,且对待插值月份前后时序数据没有严格要求,更具有广泛性。     

Detection of seismic façade damages with multi-temporal oblique aerial imagery

ABSTRACT

Remote sensing images have long been recognized as useful for the detection of building damages, mainly due to their wide coverage, revisit capabilities and high spatial resolution. The majority of contributions aimed at identifying debris and rubble piles, as the main focus is to assess collapsed and partially collapsed structures. However, these approaches might not be optimal for the image classification of façade damages, where damages might appear in the form of spalling, cracks and collapse of small segments of the façade. A few studies focused their damage detection on the façades using only post-event images. Nonetheless, several studies achieved better performances in damage detection approaches when considering multi-temporal image data. Hence, in this work a multi-temporal façade damage detection is tested. The first objective is to optimally merge pre- and post-event aerial oblique imagery within a supervised classification approach using convolutional neural networks to detect façade damages. The second objective is related to the fact that façades are normally depicted in several views in aerial manned photogrammetric surveys; hence, different procedures combining these multi-view image data are also proposed and embedded in the image classification approach. Six multi-temporal approaches are compared against 3 mono-temporal ones. The results indicate the superiority of multi-temporal approaches (up to ~25% in f1-score) when compared to the mono-temporal ones. The best performing multi-temporal approach takes as input sextuples (3 views per epoch, per façade) within a late fusion approach to perform the image classification of façade damages. However, the detection of small damages, such as smaller cracks or smaller areas of spalling, remains challenging in this approach, mainly due to the low resolution (~0.14 m ground sampling distance) of the dataset used.     

耦合ENVI-met与RAPID模型模拟3维异质植被场景亮温分布

遥感地表温度产品(LST)对陆面过程和全球与区域气候变化研究具有重要价值。但是当前卫星遥感观测到的地表温度分辨率较粗,多为混合像元,有明显的角度效应和时空变化特征,严重影响陆面过程等研究的应用精度。为定量评估3维异质性场景对亮度温度分布的影响,本文基于再分析资料,耦合3维小气候模型ENVI-met和3维热辐射传输模型RAPID,开展了地表3维温度场的模拟。研究以全球数值预报产品NCEP来提供ENVI-met所需的边界条件,分别进行了异质性植被场景的亮温水平分布和热辐射方向性模拟试验。在水平分布模拟研究中,基于机载G-LiHT数据(光学影像、激光雷达数据、LST产品)提供3维场景构建输入参数和温度场验证数据,并以美国某湿地区域的6个不同异质性场景为例进行了模拟与验证;在热辐射方向性的模拟研究中,基于机载WiDAS多角度多光谱数据构建了3维场景,并以黑河地区的2个异质性场景为例进行了模拟与验证。结果显示:(1)星下点亮温模拟值在水平分布上与G-LiHT的LST亮温值较为接近(标准误差RMSE为1.1 K),说明耦合模型能有效模拟不同空间异质性下的亮温分布。其中,裸土模拟误差最大(2.31 K),两种行播作物方向的模拟误差均小于1.2 K,道路宽度对模拟结果有影响(约为1 K);(2)耦合模型的多角度模拟结果与方向性亮温随视角的变化规律相一致,但变化速率和大小存在着差异。本文的模拟方法可以用于预测卫星过境时刻地表的热辐射方向性。     

应用FY-1D气象卫星监测雾(英文)

FY-1D is the second national operation meteorological satellite of China, and is much better compared to monitoring fog. However, research on monitoring fog using FY-1D is very few. In this paper, based on the typical FY-1D data, a fog＇s spectral characteristics in the different channels are analyzed using the histogram analysis method, and a method of monitoring fog using FY-1D is suggested. The results indicate that the 1st and 4th channels are the representative channels of FY-1D for the identification of fog. In the 1st channel, the fog is with uniform veins, smooth top, and clear-cut boundary, and its albedo is 20%-48%. In the 4th channel, the fog＇s brightness and temperature is 272-289K, and the difference value between the fog＇s and the ground surface＇s is not more than 6K.     

Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth

A commercially available digital camera can be used in a low-cost automatic observation system for monitoring crop growth change in open-air fields. We developed a prototype Crop Phenology Recording System (CPRS) for monitoring rice growth, but the ready-made waterproof cases that we used produced shadows on the images. After modifying the waterproof cases, we repeated the fixed-point camera observations to clarify questions regarding digital camera-derived vegetation indices (VIs), namely, the visible atmospherically resistant index (VARI) based on daytime normal color images (RGB image) and the nighttime relative brightness index (NRBI_NIR) based on nighttime near infrared (NIR) images. We also took frequent measurements of agronomic data such as plant length, leaf area index (LAI), and aboveground dry matter weight to gain a detailed understanding of the temporal relationship between the VIs and the biophysical parameters of rice. In addition, we conducted another nighttime outdoor experiment to establish the link between NRBI_NIR and camera-to-object distance. The study produced the following findings. (1) The customized waterproof cases succeeded in preventing large shadows from being cast, especially on nighttime images, and it was confirmed that the brightness of the nighttime NIR images had spatial heterogeneity when a point light source (flashlight) was used, in contrast to the daytime RGB images. (2) The additional experiment using a forklift showed that both the ISO sensitivity and the calibrated digital number of the NIR (cDN_NIR) had significant effects on the sensitivity of NRBI_NIR to the camera-to-object distance. (3) Detailed measurements of a reproductive stem were collected to investigate the connection between the morphological feature change caused by the panicle sagging process and the downtrend in NRBI_NIR during the reproductive stages. However, these agronomic data were not completely in accord with NRBI_NIR in terms of the temporal pattern. (4) The time-series data for the LAI, plant length, and aboveground dry matter weight could be well approximated by a sigmoid curve based on NRBI_NIR and VARI. The results confirmed that NRBI_NIR was more sensitive to all of the agronomic data for overall season, including the early reproductive stages. VARI had an especially high correlation with LAI, unless yellow panicles appeared in the field of view.     

NPP/VIIRS多时相夜光遥感影像校正方法

针对NPP/VⅡRS影像含有短暂光源和背景噪声的问题,研究了月度影像的辐射特征,提出了多时相夜光遥感影像校正方法。该方法采用峰值特征化放大影像的辐射差异,通过卷积运算区分稳定像元与异常像元,结合最大熵阈值自适应滤波分割了卷积边界的混淆噪声。试验结果表明,该方法修正了原始影像的异常波动,剔除了异常像元,影像拟合经济数据的能力明显提升,弥补了夜光数据微观小尺度研究的缺失,可为社会经济数据动态评估提供新的途径。