基于Hymap模拟的星载高光谱数据质量评价

为了有效、合理、客观地评价高光谱卫星数据质量, 充分发挥其在矿产及能源普查方面的作用, 进行了一系列研究.围绕最具代表性的3种载荷指标(几何空间分辨率、波谱分辨率及信噪比)的不同尺度, 从均方差异常、直方图异常、数据相关性异常、反射率曲线异常、信噪比参量以及该模拟数据的实际应用(蚀变信息提取和矿物填图)等多角度入手, 系统而全面地分析了模拟星载Hymap高光谱数据针对不同指标与尺度的影像质量效果.研究结果表明, 这3种载荷指标之间相互制约, 并随着空间分辨率和波谱分辨率的提高将降低图像的信噪比.当几何空间分辨率为15 m、波谱分辨率为15~20 nm, 同时信噪比≥350时, 就可以满足常规的矿物填图要求.      

基于图像融合和改进阈值的管道机器人探测图像增强方法

管道机器人探测能快速、准确和直观地识别管道结构性和功能性隐患,受管道内环境限制,探测的图像存在光照不均、对比度低和细节模糊等问题。为此,提出了一种管道机器人探测图像的增强技术。首先采用限制对比度自适应直方图均化（Contrast Limited Adaptive Histogram Equali-zation,CLAHE）和同态滤波（Homomorphic Filtering,HF）处理光照不均匀和对比度低的问题,并将2种方法结果进行融合。而后将融合的图像进行非下采样轮廓波变换（Nonsubsampled Contourlet Transform,NSCT）,并采用改进的Bayes-Shrink阈值对高频系数进行噪声去除。最后采用非线性映射函数对细节进行增强,并进行NSCT逆变换得到最终增强图像。选取5幅典型管道机器人探测图像进行增强处理,并与4种常见的图像增强技术进行对比。结果表明,基于图像融合和改进阈值的管道机器人探测图像增强技术可有效提高图像的整体和局部对比度,并有效增强图像的细节,能有效解决管道机器人探测图像存在的主要问题。      

Mapping of Neoproterozoic source rocks of the Huqf Supergroup in the Sultanate of Oman using remote sensing

In recent years, the Neoproterozoic Huqf Supergroup formations of the Oman Salt Basins have been the target for oil exploration. The present study maps the surficial exposure of the Huqf Supergroup in and around Khufai Dome of the Huqf area in the Sultanate of Oman using low-cost multi-spectral remotely sensed satellite data and image processing methods such as decorrelation stretching, principal component analysis (PCA) and spectral angle mapper (SAM), as alternative to expensive and time-consuming tools, which have the capability and potential to be used by geoscientists for oil exploration. In this research, the study of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral bands 8, 3 and 1 by decorrelation stretching well discriminated the Masirah Bay, the Khufai, the Shuram and the Buah Formations of the Nafun Group, the source rocks of Huqf Supergroup with the Quaternary deposits. The analysis of visible and near infrared–shortwave infrared spectral bands of ASTER by PCA clearly showed the occurrence and spatial distribution of such formations in the RGB principal component images (R:PC1, G:PC2, B:PC3). The spatial distributions of such formations are assessed by confusion matrix after using maximum likelihood (ML), spectral angle mapper (SAM) and spectral information divergence (SID) algorithms. The matrix of ML algorithm has provided the best overall accuracy of 92.93% and kappa coefficient of 0.92. The minerals of the formations were detected by SAM. Further, the detection of such mineral groups was confirmed through the ASTER thermal infrared (TIR) spectral indices image developed using the carbonate index (CI), quartz index (QI), and mafic index (MI). All results of image analyses are evaluated in the field and laboratory studies. The study also evaluates the satellite data and image processing methods for the formations of Jabal Akhdar, the equivalent formations of the Khufai Dome, to show the sensor capability and the use of the image processing methods to study the source rocks. The results of the study provided similar discriminations comparable to the Khufai Dome. Therefore, the data and the techniques are recommended to the exploration geologists for use in similar regions of the world.     

藏南拉轨岗日变质核杂岩带三层结构的影像证据及意义

拉轨岗日变质核杂岩带是喜马拉雅造山带的组成部分,从几何学角度查明其结构是该变质核杂岩带研究的一个重要内容。利用遥感技术来宏观地研究该变质核杂岩带的空间结构及物质组成,是对野外调研成果的有效验证和有益补充。从遥感图像信息提取的结果可以看出,变质核、过渡带及盖层在色调(色彩)、空间形态、水系类型与分布状况、影纹图案等各个方面均存在不同程度的差异。这些光谱及空间特征的综合使得变质核杂岩的三层结构被清晰地显现出来,尤其是过渡带的弱信息也在有TM6加入的K-L变换等图像处理手段中被有效地提取出来,从而更确切地证明该变质核杂岩带具有三层结构。同时,结合已有资料从遥感信息机理方面探讨三层结构各自的物质组成及其演变,为西部地区深入分析区域地质构造提供了一种有效的方法。     

基于Worldview-3与Sentinel-1 SAR数据的草原矿区复垦植被生物量反演方法研究

利用多源遥感数据定量反演矿区复垦植被生物量是高效、动态、大面积监测土地复垦和生态恢复效果的必要手段之一。本文以内蒙古草原露天煤矿为研究区,联合遥感光学与雷达数据各自的优势,探索基于Worldview-3(WV-3)与Sentinel-1 SAR数据的矿区复垦植被生物量反演方法,选择主成分-小波变换(W-PCA)算法对WV-3与Sentinel-1 SAR数据进行融合,揭示波段反射率、植被指数、后向散射系数及纹理特征等参数与生物量之间的相关关系,建立多变量的生物量反演模型,并分析不同生物量模型的空间不确定性。结果表明:(1)通过W-PCA算法得到融合后的图像,信息熵的提高反映了融合图像与光学WV-3图像相比具有更多的细节信息,平均梯度的提高反映了融合图像与Sentinel-1 SAR图像相比具有更高的清晰度和更丰富的纹理信息。融合后的第8波段相关系数最高、光谱扭曲度最低、光谱保真度最高。(2)通过相关性分析,发现增强型植被指数(EVI)、归一化植被指数(NDVI)、VH极化、VH均值纹理以及融合后第8波段与生物量显著正相关。WV-3的NDVI与Sentinel-1的VH_ME建模精度(R²=0.834 0,RMSE=16.464 6 g/m²,Ac=81.52%)最高,融合后的第8波段验证精度(R²=0.798 3,RMSE=22.828 3 g/m²,Ac=74.64%)最高。(3)基于不同模型的残差不确定性分析,Sentinel-1 SAR数据变量建立的模型估测结果更容易出现高估及饱和现象,两者联合变量建立的模型可实现优势互补,利用融合数据建立的模型明显改善生物量小于40 g/m²时的高估计现象以及生物量大于100 g/m²时的两者饱和现象,其不确定性降低2.42~9.68 g/m²。因此,利用光学和雷达遥感融合能够有效提高复垦植被生物量的估算精度,为草原矿区复垦植被精细监测提供有效的数据支持。     

Methods for Enhancing Geological Structures inSpectral Spatial Difference—Based on Remote-Sensing Image

In this paper,som e image processing methods such asdirectional template (mask) matching enhancement,pseudo-color or false color enhancement,K- L transform enhancementare used to enhance a geological structure,one of importantore- controlling factors,shown in the remote- sensing images.This geological structure is regarded as image anomaly in theremote- sensing image,since considerable differences,basedon the spatial spectral distribution pattern,in gray values(spectral) ,color tones and textu…     

1960—2017年中国降水集中程度特征分析

降水集中程度是反映降水结构的重要指标。基于1960—2017年中国773个气象站日降水资料,运用降水集中程度（Q）,研究中国降水集中程度的时空特征,分析其与降水量、海洋状态之间的关系。结果表明:①中国年平均Q值为0.38,南北较低中间较高;冬季和秋季的降水集中程度相对较高,平均Q值分别为0.53和0.51,夏季和春季相对较低,分别为0.39和0.48。②年降水集中程度变化趋势较小,总体上略有上升,东南升高西北降低;在年和季尺度,Q和降水量均表现出较强的负相关性,年尺度相关系数为-0.71,秋季的相关性最强,相关系数为-0.89,春季的相关性最弱,相关系数为-0.70,降水集中程度和降水量共同影响水旱灾害受灾面积。③Q与NINO3.4指数间的相关性随着滞后时间的延长先增大后减小,当滞后时间为2个月时相关系数最大,平均为0.13,由北向南总体呈"-+-"分布;与PDO指数间的相关系数随着滞后时间的延长先减小后增大再减小,当滞后时间为4个月时相关系数最大,平均为0.12,以负相关为主。     

消除噪声的RNF图像融合算法

在多光谱遥感图像中有时也会存在较严重的随机点噪声的干扰，这种随机点噪声严重影响了地物光谱特征提取和识别的精度，降低了各种遥感定量分析方法技术的有效性。常用的遥感图像随机噪声消除或压抑方法是Fourier变换频率域方法和采用平滑模板对图像进行卷积处理的空间域方法，但它们往往会损失图像信息。文章探讨了消除或压抑噪声的图像融合方法，即RNF融合法。RNF融合算法先对参与融合的多光谱图像进行低通滤波，对全色波段进行高通滤波。然后将滤波后的全色波段与多光谱图像用HSI变换法进行融合，融合后的图像消除了噪声。     

一种面向对象的最优分割尺度计算模型

作为信息提取和分类的前提,面向对象的影像分割尺度参数的设置直接影响到提取和分类的精度。本文以GF-2影像数据为例,在已有分割理论和方法的基础上提出一种基于最优分割尺度的计算模型（OS模型）。该模型以主成分分析所得的主成分以及新建的归一化植被指数（normalized vegetation index,NDVI）特征层作为分割参考层,综合考虑均质因子的影响,构建加权尺度评价指数,插值拟合最优分割尺度。构建误差系数（E_c）对模型进行评价,结果表明：OS模型误差系数（E_c=1.15%）小于传统模型（E_c=3.28%）,且分割对象更均匀、与实际地物更接近。     

Landsat8 OLI图像增强与岩性识别方法

新疆维吾尔自治区阿克陶县布伦口地区海拔高、穿越条件差、岩性复杂,因而研究Landsat8 OLI数据信息增强与岩性识别在该区有强大的应用需求,也有遥感岩性识别的理论意义。区内不同岩性单元光谱较为相似,仅利用最佳波段组合(OIF)指数、主成分分析(PCA)和独立主成分分析(ICA)等图像增强方法均不能最大限度地区分各岩性单元。通过分析独立主成分变换之后各岩性单元的特征向量,发现不同岩性单元特征向量差异比原图像光谱差异增大,为扩大差异,依据特征向量统计结果构建比值运算,再联合PCA特征向量进行彩色合成,各岩性单元可识别性显著增加。研究表明:(1)采用联合多种图像增强的方法能将研究区不同岩性单元进行分离;(2)国道314南北两侧地层均划归布伦阔勒岩群的结论有误,其北侧仍属布伦阔岩石,南侧实为片麻状二长花岗岩;(3)沙湖南东原划归志留纪的岩石单元可进一步分解为志留纪地层和元古代布伦阔勒岩群岩石单元。该方法有推广应用价值和深化研究的前景。     

应用多种数据融合方法对多时相SPOT图像处理提取三峡工程大坝区地面动态变化信息

三峡库区三斗坪大坝区及其周围地区在1994到1998年期间，由于大坝的施工地面发生了巨大变化。这些变化在1990、1997和1998年获取的SPOT遥感图像上都有明显的记录，其中一些地面变化细节在分辨率10m的全波段（P）图像更为清楚。这里利用三种方法（高通滤波算法（HPF algorithm，Chavezet al．1991）、RWM—ARSIS方法（Ranchin and Wald，1999）和P＋XS算法（Anonymous 1986））对三峡工程三斗坪大坝区的多时相高空间分辨率的SPOT图像（获取时闻为1990，1997和1998年，包括全波段P图像和多波段XS图像）进行融合处理，在提取测区地面动态变化信息的同时，从理论和实际应用的角度探讨了不同数据融合方法的效果。     

基于WorldView-Ⅱ高分数据的盐湖矿化度含量定量反演研究——以尕斯库勒盐湖为例

鉴于盐湖水体矿化度含量定量反演研究较少,且中、低空间分辨率遥感数据反演的矿化度含量精度偏低,文章以柴达木盆地西部尕斯库勒盐湖为例,基于WorldView-Ⅱ高空间分辨率遥感数据和实测矿化度数据,开展了盐湖湖表水体矿化度含量定量反演技术研究。通过开展盐湖水际线提取、盐湖水体矿化度光谱诊断特征分析、矿化度识别遥感指数构建、矿化度遥感信息分离和线性回归模拟,构建了盐湖矿化度含量定量反演模型。经评价,模型反演精度为96.61%。研究结果表明,基于WorldView-II高分数据定量反演盐湖矿化度含量的方法是有效的,该方法对于快速定量识别盐湖矿化度含量,降低盐湖矿化度调查和分析成本,预测富矿水域具有十分重要的意义。     

基于Hyperion数据的油砂分布遥感研究

依据油砂中烃类的微渗漏和油砂组分光谱特征响应原理,利用Hyperion高光谱影像提取和识别与油砂分布相关的波谱信息,进行非常规油气能源--油砂分布的有利区预测。根据油砂所致烃类微渗漏的地表特征可知,低植被覆盖区的异常以矿物异常为主,中、高植被覆盖区的异常以植被异常为主。利用归一化植被指数表征地表植被的不同覆盖程度：当其值为[0.0,0.4）时,采用SAM（spectral angle method）提取矿物异常信息;当值为[0.4,0.7]和（0.7,1.0]时,分别采用LIC（lichenthaler index）和CTR（carter indices）方法提取植被异常信息。同时,为确保提取的矿物和植被异常信息的产生是由油砂中烃类的微渗漏所导致,以野外油砂反射光谱为端元,运用光谱角分类方法提取油砂信息,并将其与获取的矿物和植被异常信息进一步应用空间叠置分析确定油砂分布有利区。结果表明,综合运用野外实测高光谱数据和高光谱影像数据能够较准确地预测出研究区中油砂的分布位置。因此,应用高光谱影像进行油砂分布的有利区预测,可为未来利用遥感技术深入研究油砂可采储量评价提供参考依据。     

基于神经网络模型的中国表层土壤有机质空间分布模拟方法

基于全国第二次土壤普查得到的6 241个典型土壤剖面数据,采用主成分分析方法和径向基函数神经网络模型建立不同植被类型—土纲单元内土壤有机质与气候、地形和植被等环境因子间的非线性关系,模拟全国表层土壤有机质的空间分析格局。结果表明,该模型具有较准确的预测能力,性能指数达到1.94。与普通克里格法、反比距离法和多元回归模型相比,神经网络模型对621个验证点模拟结果与实测值的相关系数为0.799,分别提高了0.265、0.181和0.120,平均绝对误差分别降低了4.25、4.43和2.34 g/kg,平均相对误差分别降低了30.16%、32.66%和5.93%,均方根误差则分别降低了8.61、8.24和6.24 g/kg;从模拟结果图来看,神经网络模型能够提供更多的细节信息。该方法为大尺度土壤性质空间分布模拟提供了有益的参考。     

基于DEM的天山山区气温和降水序列推算方法研究

为了计算天山山区区域气候要素平均值序列,提出了一个新的气候序列计算方案.对天山山区17个气象站和10个水文站的1961-2000年的年气温、降水资料进行了自然正交分解(EOF),并以DEM(Digital Elevation Model)的1 km×1 km网格数据为基础,结合多元回归等方法,分别建立了前3个特征向量与经度、纬度及海拔高度因子的插值模型,由此推算出天山山区(海拔≥1500 m)年平均气温、降水序列.误差分析表明,27站实测的年平均气温序列与计算的区域平均序列值的相关系数为0.996,系统性偏差小,平均相对误差为5.5%;年降水量序列的实测值与计算值平均相对误差略大,为14.8%,相关系数为0.972.区域平均序列的计算值与测站简单的算术平均序列在量值上存在明显的差异,计算出的气温平均偏低4.3℃,降水量平均偏高43.2 mm.此方法为计算站点稀少、地形复杂的区域平均的要素时间序列方面提供了一个解决手段.     

Spectral wheat yield prediction modeling using SPOT satellite imagery and leaf area index

As wheat represents the main staple food and strategic crop in Egypt and worldwide and since remote sensing satellite imagery is the tool to obtain synoptic, multi-temporal, dynamic, and time-efficient information about any target on the Earth, the main objective of the current study is to use remote sensing satellite imagery to generate remotely sensed empirical preharvest wheat yield prediction models. The main input parameters of these models are spectral data either in the form of spectral reflectance data released from Satellite Pour l’Observation de la Terre (SPOT) 4 satellite imagery or in the form of spectral vegetation indices. The other input factor is leaf area index (LAI) that was measured by LAI Plant Canopy Analyzer. The four spectral bands of SPOT4 imagery are green, red, near-infrared, and middle infrared; the five vegetation indices that are forms of ratios between red and near-infrared bands are normalized difference vegetation index, ratio vegetation index, soil-adjusted vegetation index, difference vegetation index, and infrared percentage vegetation index. Another vegetation index is green vegetation index that is calculated through a ratio between green band and near-infrared band. Each of the above-mentioned factors was used as an input factor against wheat yield to generate wheat yield prediction models. All generated models are site-specific limited to the area and the environment and could be applicable under similar conditions in Egypt. The study was carried out in Sakha experimental station by using the dataset from two wheat season 2007/2008 and 2009/2010. The total wheat area was 1.3 ha cultivated by Sakha 93 cultivar. Modeling and validation process were carried out for each season independently. Modeled yield was tested against reported yield through two common statistical tests; the standard error of estimate between modeled yield and reported yield, and the correlation coefficient for a direct regression analysis between modeled and reported yield with each generated model. Generally, as shown from the correlation coefficient of the generated models, green and middle infrared bands did not show good accuracy to predict wheat yield, while the other spectral bands (red and near-infrared) bands showed high accuracy and sufficiency to predict yield. This was proven through the correlation coefficient of the generated models and through the generated models with the wheat crops for the two seasons. Accordingly, the green vegetation index that is generally calculated from green and near-infrared bands showed relatively lower accuracy than the rest of the vegetation index models that are calculated from red and near-infrared bands. LAI showed high accuracy to predict yield as shown from the statistical analysis. The models are applicable after 90 days from sowing stage and applicable in similar regions with the same conditions.     

基于地物光谱分析的WorldView-2数据岩性识别:以新疆乌鲁克萨依地区为例

针对新疆维吾尔自治区西昆仑乌鲁克萨依地区的岩性出露情况,采用WorldView-2遥感影像数据进行岩性识别研究。在分析研究区内不同岩性的实测光谱曲线特征的基础上,针对在岩性识别过程中出现的不同岩性色彩反差弱、异物同谱、岩层表层浅覆盖等一系列问题,结合高空间分辨率WorldView-2数据波段特点和数据统计特征,确定了最佳波段组合,并采用主成分分析降低了波段间的高相关性。在此基础上进行反差增强、纹理增强等处理,提高了不同岩性对比度,去除了外界因素对光谱信息的干扰,为精确识别不同岩性提供了可靠依据。     

GLDAS月降水数据在中国区的适用性评估

全球陆面数据同化系统(GLDAS)是全球变化与水循环研究的重要数据源之一.对比分析了1979—2012年间GLDAS多套降水数据与中国地面观测逐月降水数据所反映的中国降水趋势变化空间特征,采用相关系数、平均偏差、相对绝对误差和均方根误差4个指标,从时间变化和空间分布特征两个方面,对GLDAS降水数据在中国区域的数据质量进行了系统评估.结果表明:GLDAS-1的几套数据在时间上具有明显不连续性,1996年数据质量严重异常,2000年数据质量也较差,而且,不论是GLDAS-1数据,还是GLDAS-2数据,都存在前期(1979—1995年)与实测数据吻合度高于后期(1997年以后)的现象;GLDAS数据在中国东部湿润区的质量高于在西部干旱区;从相关性与误差指标来看,GLDAS-1数据质量略优于GLDAS-2(主要体现在1995年以前时段),但是GLDAS-2在数据一致性、数据质量季节稳定性及对趋势性描述能力方面则明显优于GLDAS-1数据.     

Model-based remote sensing algorithms for particulate organic carbon (POC) in the Northeastern Gulf of Mexico

Hydrographic data, including particulate organic carbon (POC) from the Northeastern Gulf of Mexico (NEGOM) study, were combined with remotely-sensed SeaWiFS data to estimate POC concentration using principal component analysis (PCA). The spectral radiance was extracted at each NEGOM station, digitized, and averaged. The mean value and spurious trends were removed from each spectrum. De-trended data included six wavelengths at 58 stations. The correlation between the weighting factors of the first six eigenvectors and POC concentration were applied using multiple linear regression. PCA algorithms based on the first three, four, and five modes accounted for 90, 95, and 98% of total variance and yielded significant correlations with POC with R ² = 0.89, 0.92, and 0.93. These full waveband approaches provided robust estimates of POC in various water types. Three different analyses (root mean square error, mean ratio and standard deviation) showed similar error estimates, and suggest that spectral variations in the modes defined by just the first four characteristic vectors are closely correlated with POC concentration, resulting in only negligible loss of spectral information from additional modes. The use of POC algorithms greatly increases the spatial and temporal resolution for interpreting POC cycling and can be extrapolated throughout and perhaps beyond the area of shipboard sampling.     

Utilization of ASTER and OLI data for lithological mapping of Nugrus-Hafafit area,South Eastern Desert of Egypt

Lithological discrimination of Neoproterozoic rocks occupying Nugrus-Hafafit area, South Eastern Desert of Egypt, has been carried out using Operational Land Imager (OLI) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensors’ imagery data. The applicable processing enhancement techniques include optimum index factor (OIF), band ratioing, principal component analysis (PCA), and minimum noise fraction (MNF) transform. The area comprises varieties of low-grade metamorphosed ophiolitic mélange and island-arc rocks, thrusting over high-grade metamorphic gneissic core complexes, and intruded by syn-, late-, and post-tectonic granitoids. The OLI band ratio 6/7 discriminates clearly the ophiolitic serpentinites-talc-carbonate rocks, while 4/5 ratio image is able to separate between mafic and felsic rocks. Moreover, the ASTER band ratio 6/8 is used to distinguish the amphibole-bearing rocks, including amphibolite and hornblende gneiss. The OLI and ASTER second principal component (PC2) images reflect the contrast spectral behavior of ophiolitic mélange rocks through visible-near-infrared (VNIR) and shortwave (SWIR) regions. The OLI-PC3 shows the ability to delineate the Fe-rich rocks, including amphibolite and metamafics, while ASTER-PC3 is effective for quartz-feldspathic granites and psammitic gneisses. Visual interpretation and integration of processed data with petrography and field investigation resulted in complete differentiation for the different lithologies and creation of a new detailed geological map of Nugrus-Hafafit area.