Landsat image data quality studies

The July 1982 launch of Landsat-4 was immediately followed by a two-year comprehensive set of detailed investigations sponsored by the National Aeronautics and Space Administration (NASA) at Goddard Space Flight Center (GSFC). The Landsat Image Data Quality Analysis (LIDQA) research plans for these investigations were specified prior to launch, so that minimum time would be lost in assessing the performance of the long-awaited Thematic Mapper (TM) sensor that Landsat-4 carried in addition to a fourth Multispectral Scanner (MSS). The LIDQA investigations have been substantially completed, and have shown that the TM is a very good spaceborne multispectral radiometer, and has met or exceeded most of its design goals. TM's new short-wave infrared (SWIR) spectral capability yielded improved mineral and plant discrimination compared to the MSS, as anticipated by ground-based and airborne TM simulations. Moreover, the improved spatial resolution and geometric accuracy of Landsat-4 and the TM have resulted in satellite image maps exceeding 1:100,000 U.S. map accuracy standards. Finally, based on an information entropy measure, principal component analysis, and classification results, TM data has been shown to approach its theoretical limit in information content per pixel, exceeding the MSS by at least a factor of two.     

A comparison of atmospheric aerosol measurements by various satellite sensors

Investigations to measure the vertical optical thickness of aerosols over ocean surfaces has been conducted using several different satellite sensors. Landsat 1 and Landsat 2 data originally confirmed that a linear relationship exists between the upwelling visible radiance and the aerosol optical thickness (about 90% of this thickness is generally in the lowest 3 km of the atmosphere). Similar relationships have also been found for sensors on GOES-1, SMS-2, NOAA-5, and NOAA-6 satellites. The linear relationship has been shown theoretically to vary with the aerosol properties, such as size distribution and refractive index, although the Landsat data obtained at San Diego showed little variability in the relationship. The differences between the results found for the various satellite sensors are discussed, and are attributed mainly to uncertainties in the calibration of the sensors. To investigate the general applicability of the technique to different locations, a global-scale ground truth experiment was conducted with the AVHRR sensor on NOAA-6 to determine the relationship at eleven ocean sites around the globe. Analysis of the data shows good agreement between the satellite and ground truth values of the aerosol optical thickness, and indicates that the technique has global application. At two of the sites, multispectral radiometric measurements of the Junge aerosol size distribution parameter were made, and showed good agreement with a value inferred from the AVHRR Channels 1 and 2 radiances.     

Mineral mapping in the Kap Simpson complex,central East Greenland,using HyMap and ASTER remote sensing data

This research focuses on the application of HyMap airborne hyperspectral data and ASTER satellite multispectral data to mineral exploration and lithologic mapping in the Arctic regions of central East Greenland. The study area is the Kap Simpson complex in central East Greenland. The Kap Simpson complex is one of the largest exposed Palaeogene felsic complexes of East Greenland. It has been the target of several mineral exploration projects. The analysis of the HyMap data produced a detailed picture of the spatial distribution of the alteration minerals in the Kap Simpson complex, unavailable from field-based studies in the area. The analysis of the ASTER data produced mineral maps which due to the moderate spatial and spectral resolution of the ASTER imagery can be useful for reconnaissance level mineral exploration. Colour composites of the mean normalized ASTER thermal bands display lithological information and detected a large felsic igneous intrusion that has not been shown on the recently compiled geological maps of the area. The results of this research have considerable potential to evaluate the use of hyperspectral and multispectral remote sensing for geological purposes in the Arctic regions of central East Greenland.     

Detailed choice of the spectral bands

The SPOT satellite is designed so as to provide multispectral observations with 20 m resolution and panchromatic observation with 10 m resolution. The three spectral bands allow a study of the spectral signature of objects; so, the detailed profile of these bands has been optimized so as to select the parts of major interest in the reflectance of various objects and minimize the atmospheric perturbation in the quantitative measurement. The panchromatic band is mainly involved in cartographic application; so the design of the spectral sensitivity is related to the study of the contrast between objects thematically characterized, taking into account the actual resolution.     

An object-level strategy for pan-sharpening quality assessment of high-resolution satellite imagery

Current satellite imaging systems offer a trade-off between high spatial and high spectral resolution providing panchromatic images at a higher spatial resolution and multispectral images at a lower spatial resolution but rich in spectral information while a wide range of applications need the highest level of this information, simultaneously. Image fusion techniques as means of enhancing the information content of initial panchromatic and multispectral images produce new images, titled pan-sharpened, which inherent the advantages of the initial images. Considering the impact of fusion accuracy on the quality of corresponding applications, it is necessary to evaluate the quality of these processed images. During the last decade, a lot of quality evaluation metrics have been proposed which are mostly inspired by traditional image quality metrics. These methods are mostly based on applying quality metrics at the pixel level and evaluating final quality value based on averaging of obtained metric values through the whole image. However, obtained results clearly show that the behaviour of image fusion quality is inconsistent amongst different image objects. In this article, by applying image fusion quality metrics (IFQMs) to image objects, an object-level strategy for quality assessment of the image fusion process is proposed. The proposed strategy is applied to different satellite imagery covering residential and agricultural areas. Experimental results show higher capabilities of object-level quality assessment strategy in the quality assessment of the fusion process. Evaluating fusion quality at the object level provides the potential of fusion quality assessment for each individual image object in compliance with different parameters such as the type of objects and the effective size of objects in data set.     

A modern robust approach to remotely estimate chlorophyll in coastal and inland zones

The chlorophyll concentration of a water body is an important proxy for representing the phytoplankton biomass. Its estimation from multi or hyper-spectral remote sensing data in natural waters is generally achieved by using (i) the waveband ratioing in two or more bands in the blue-green or (ii) by using a combination of the radiance peak position and magnitude in the red-near-infrared (NIR) spectrum. The blue-green ratio algorithms have been extensively used with satellite ocean color data to investigate chlorophyll distributions in open ocean and clear waters and the application of red-NIR algorithms is often restricted to turbid productive water bodies. These issues present the greatest obstacles to our ability to formulate a modern robust method suitable for quantitative assessments of the chlorophyll concentration in a diverse range of water types. The present study is focused to investigate the normalized water-leaving radiance spectra in the visible and NIR region and propose a robust algorithm (Generalized ABI, GABI algorithm) for chlorophyll concentration retrieval based on Algal Bloom index (ABI) which separates phytoplankton signals from other constituents in the water column. The GABI algorithm is validated using independent in-situ data from various regional to global waters and its performance is further evaluated by comparison with the blue-green waveband ratios and red-NIR algorithms. The results revealed that GABI yields significantly more accurate chlorophyll concentrations (with uncertainties less than 13.5%) and remains more stable in different waters types when compared with the blue-green waveband ratios and red-NIR algorithms. The performance of GABI is further demonstrated using HICO images from nearshore turbid productive waters and MERIS and MODIS-Aqua images from coastal and offshore waters of the Arabian Sea, Bay of Bengal and East China Sea.     

地球敏感器光谱波段对红外带通滤光片的影响

从卫星用红外带通滤光片的膜系设计出发,分析了地球敏感器的不同光谱波段（工作波段）对锗基片和碲片铅镀膜材料的吸收和透射率的影响,以及对滤光片膜系的等效折射率和反射损失的影响。给出了设计结果,提出了卫星用地球敏感器的最佳光谱波段。     

Rainfall estimation over a Mediterranean region using a method based on various spectral parameters of SEVIRI-MSG

The ultimate objective of this paper is the estimation of rainfall over an area in Algeria using data from the SEVIRI radiometer (Spinning Enhanced Visible and Infrared Imager). To achieve this aim, we use a new Convective/Stratiform Rain Area Delineation Technique (CS-RADT). The satellite rainfall retrieval technique is based on various spectral parameters of SEVIRI that express microphysical and optical cloud properties. It uses a multispectral thresholding technique to distinguish between stratiform and convective clouds. This technique (CS-RADT) is applied to the complex situation of the Mediterranean climate of this region. The tests have been conducted during the rainy seasons of 2006/2007 and 2010/2011 where stratiform and convective precipitation is recorded. The developed scheme (CS-RADT) is calibrated by instantaneous meteorological radar data to determine thresholds, and then rain rates are assigned to each cloud type by using radar and rain gauge data. These calibration data are collocated with SEVIRI data in time and space.     

Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data

Satellite data, taken from the National Oceanic and Atmospheric Administration (NOAA) have been proposed and used for the detection and the cartography of vegetation cover in North Africa. The data used were acquired at the Analysis and Application of Radiation Laboratory (LAAR) from the Advanced Very High Resolution Radiometer (AVHRR) sensor of 1 km spatial resolution. The Spectral Angle Mapper Algorithm (SAM) is used for the classification of many studies using high resolution satellite data. In the present paper, we propose to apply the SAM algorithm to the moderate resolution of the NOAA AVHRR sensor data for classifying the vegetation cover. This study allows also exploiting other classification methods for the low resolution. First, the normalized difference vegetation index (NDVI) is extracted from two channels 1 and 2 of the AVHRR sensor. In order to obtain an initial density representation of vegetal formation distribution, a methodology, based on the combination between the threshold method and the decision tree, is used. This combination is carried out due to the lack of accurate data related to the thresholds that delimit each class. In a second time, and based on spectral behavior, a vegetation cover map is developed using SAM algorithm. Finally, with the use of low resolution satellite images (NOAA AVHRR) and with only two channels, it is possible to identify the most dominant species in North Africa such as: forests of the Liege oaks, other forests, cereal’s cultivation, steppes and bar soil.     

Use of satellite images to obtain accurate snowmelting runoff forecasts and to survey geothermal activity along Los Andes Range,Chile

Pilot experiments developed in Los Andes Range, Central Zone of Chile, have obtained significant snd positive preliminary results on the use of multispectral and multitemporal satellite images to prepare snowmelt runoff forecasts by measuring snowcovered area in Andean watersheds. The same information is also very useful for studying regional and semi-regional geothermal activity along Los Andes in Chile. These results will be improved with the new satellite data to be available in the next years.     

Main results and perspectives of some Chilean experiences developed with low cost and accurate spatial remote sensing technology

This paper intends to summarize the main results and perspectives of several Chilean Programs developed by using low cost but accurate remote sensing techniques. Due to paper restrictions, three main applications will be shown : use of satellite data collection systems to measure meteorological data in Ant arctic Peninsula; study of geothermal resources in Los Andes Range by using multispectral and multitemporal Landsat images; and snowmelt runoff forecasting for Andean watersheds by using Landsat data. All these applications have allowed to obtain important and useful results and low cost, reliable and accurate methodologies have been obtained for these studies.     

材料粗糙度变化对卫星光谱红化效应的影响

在轨人造目标的地基观测光谱存在红化效应,影响对目标反射光谱的理解应用.目标在进入空间环境后外露材料表面粗糙度发生变化.本文结合材料的光谱散射特性、卫星轨道和观测条件,仿真分析了卫星表面材料粗糙度变化对红化效应的影响.基于文献中实验数据,设计不同粗糙度的卫星表面材料样本,模拟空间环境中材料表面粗糙度的变化.利用双向反射分布函数,建立不同粗糙度材料样本的光谱散射模型.理论模拟结果表明,材料表面粗糙度的变化会影响其光谱散射特性,散射光谱形态和散射分布取决于粗糙度及入射-反射几何关系.引入网格剖分和可见面片,利用材料双向反射分布函数,仿真计算卫星表面材料在不同粗糙度状态下,整个卫星在可见光波段的反射光谱信号.仿真结果表明,随着卫星表面材料粗糙度的增加,其反射光谱在600nm之后随波长增加而呈现上升趋势,这表明空间环境中外露材料表面粗糙度变化是卫星产生红化效应的原因之一.      

基于梯度提升决策树的量子科学实验卫星光学实验预测

量子科学实验卫星在轨运行期间完成4种光学实验，地面监测人员通过遥测参数阈值判断卫星是否进行光学实验、实验类型及实验结果.这种方法需要预先设定大量阈值，并且这些阈值需要根据在轨卫星重新设定，可扩展性较差.针对以上问题，提出一种基于机器学习的光学实验判别方法，将量子科学实验卫星的光学实验监测任务抽象为机器学习中的多元分类问题，构建分类模型，利用量子科学实验卫星的真实历史遥测数据对模型进行训练，并通过真实实验计划对训练得到的模型进行验证.实验结果表明，本文提出的方法在没有专家先验知识的前提下，判别准确率达到99%，可用于量子科学实验卫星光学实验的实时监测任务.提出的基于机器学习的判别方法具有较强的可扩展性，可应用于卫星在轨运行的其他监测任务.      

多光谱遥感之最佳波段选择

本文提出一种以提供最大信息量为准则的最佳遥感波段选择方法.由于不同波段信号之间存在较强的相关性, 因此本方法从地面主要目标的光谱特性出发, 计算出所有可能使用波段之间的全部相关系数.假设信号是近似正态分布的, 就可以从相关矩阵的行列式计算出每个波段的条件信息量.比较各种可能波段组合的信息量, 就可以找出最佳的波段组合.文中也介绍了一种比较简单的方法, 可以得到“准最佳”的波段选择.最后, 对遥感卫星设计时遥感波段的选择问题做了一些探讨.      

U.S. operational space program for climate observations

Satellites provide two important characteristics to earth climate studies not available from other, conventional sources: (1) full global coverage, and (2) consistency within the data set. This latter arises from the fact that the satellite data are usually derived from one instrument (or at least from a small number) whereas other sources involve large numbers of separate instruments and hence exhibit a substantial standard deviation. Satellite data, of course, are more subject to bias and must therefore be carefully validated, usually via ground truth.The ISCCP and ISLSCP are examples of the increasing reliance on satellite data for climate studies. In addition to the multispectral images, quantitative products of importance are: (1) atmospheric temperature structure, (2) snow cover, (3) precipitation, (4) vegetation index, (5) maximum/minimum temperature, (6) insolation, and (7) earth radiation balance. The U.S. civil space program is presently committed to its current geostationary (GOES) and polar (NOAA) programs through this decade and to continue both programs into the next decade with spacecraft carrying improved and augmented instrumentation. GOES VISSR Atmospheric Sounder (VAS) data, presently in research status and available only for special observation periods, will become available operationally in 1987 from the current spacecraft series. GOES-Next will provide additional spectral channels, simultaneous imaging, atmospheric soundings, and possibly increased resolution starting in 1990. The NOAA follow-on spacecraft, in the same time frame, is expected to provide additional spectral channels, improved passive microwave radiometry, and possibly increased spatial resolution. The Landsat program is expected to be continued by a commercial operator following the useful life of Landsat-5. All three follow-on programs are presently at various stages of definition and procurement. Final definition may not be completed until late in 1984. However, their status as of the time of this presentation will be reviewed in detail.     

A study of backscattered spectra dynamics of agricultural crops during growth period on the territory of the Krasnoyarskii Krai (Russia)

The work presents the results of the study aimed at determining the seasonal dynamics of the spectral brightness and reflectance of agricultural crops (wheat, barley and oats) in the Krasnoyarskii Krai (Russia). The analysis of spectral curves obtained through field ground measurements and from satellite data showed that fine spectral differences can be used to study the spatial distribution of various types of vegetation and their ecological state. Based on the created electronic spectral brightness data base, the possibilities are shown of using spectrophotometric information for determining morphophysiological changes occurring in the plants and their species composition. The determined contrasts can be effectively used to obtain necessary information while processing space images, which suffer from natural interferences (varying optical thickness of the atmosphere, cloudiness, alterations in the scanner’s angle of view, varying solar height, and highly inhomogeneous underlying surface).     

ASTER spectral sensitivity of carbonate rocks – Study in Sultanate of Oman

Remote sensing satellite data plays a vital role and capable in detecting minerals and discriminating rock types for explorations of mineral resources and geological studies. Study of spectral absorption characters of remotely sensed data are under consideration by the exploration and mining companies, and demonstrating the spectral absorption characters of carbonates on the cost-effective multispectral image (rather than the hyperspectral, Lidar image) for easy understanding of all geologists and exploration communities of carbonates is very much important. The present work is an integrated study and an outcome of recently published works on the economic important carbonate rocks, includes limestone, marl, listwaenites and carbonatites occurred in parts of the Sultanate of Oman. It demonstrates the spectral sensitivity of such rocks for simple interpretation over satellite data and describes and distinguishes them based on the absorptions of carbonate minerals in the spectral bands of advanced spaceborne thermal emission and reflection radiometer (ASTER) for mapping and exploration studies. The study results that the ASTER spectral band 8 discriminates the carbonate rocks due to the presence of predominantly occurred carbonate minerals; the ASTER band 5 distinguishes the limestones and marls (more hydroxyl clay minerals) from listwaenite (hydrothermally altered rock) due to the presence of altered minerals and the ASTER band 4 detects carbonatites (ultramafic intrusive alkaline rocks) which contain relatively more silicates. The study on the intensity of the total absorptions against the reflections of these rocks shows that the limestones and marls have low intensity in absorptions (and high reflection values) due to the presence of carbonate minerals (calcite and dolomite) occurred in different proportions. The listwaenites and carbonatites have high intensity of absorptions (low reflection values) due to the occurrence of Mn-oxide in listwaenites and carbonates in carbonatites apart the influence of major carbonate minerals that occurred predominantly in these rocks. The study of ASTER thermal infrared (TIR) spectral bands distinguished the marls have low emissivity of energy due to the presence of hydroxyl bearing alumina-silicate minerals from the other rocks such as limestones, listwaenites and carbonatites which have high emissivity due to the absence of hydroxyl bearing alumina-silicate minerals and the presence of carbonate minerals and carbonates.     

基于自适应注入模型的遥感图像融合方法

遥感图像融合的目的是融合高光谱分辨率、低空间分辨率的多光谱（MS）图像和高空间分辨率、低光谱分辨率的全色（PAN）图像,得到高光谱分辨率与高空间分辨率的融合图像。遥感图像的注入模型中如何确定注入细节及注入系数是该技术研究的关键。针对注入细节优化,先通过模拟MS传感器的特性来定义一种多尺度高斯滤波器,再用该滤波器卷积PAN图像以提取细节,得到与MS图像高度相关的细节。针对注入系数优化,综合考虑光谱信息与细节信息提出一种自适应的注入量系数。为更好地保留边缘信息,提出一种新的边缘保持权重矩阵,实现光谱信息与空间的双保真。将优化后的注入系数与注入细节相乘注入到上采样后的MS图像中,得到融合结果。对所提方法进行性能分析,并在各卫星数据集上进行大量测试,与一些先进的遥感图像融合方法进行对比,实验结果表明,所提方法在主观与综合客观指标上都能达到最优。      

Distribution of chlorophyll and harmful algal blooms (HABs): A review on space based studies in the coastal environments of Chinese marginal seas

Monitoring of spatial and temporal distribution of chlorophyll (Chl-a) concentrations in the aquatic milieu is always challenging and often interesting. However, the recent advancements in satellite digital data play a significant role in providing outstanding results for the marine environmental investigations. The present paper is aimed to review ‘remote sensing research in Chinese seas’ within the period of 24 years from 1978 to 2002. Owing to generalized distributional pattern, the Chl-a concentrations are recognized high towards northern Chinese seas than the southern. Moreover, the coastal waters, estuaries, and upwelling zones always exhibit relatively high Chl-a concentrations compared with offshore waters. On the basis of marine Chl-a estimates obtained from satellite and other field measured environmental parameters, we have further discussed on the applications of satellite remote sensing in the fields of harmful algal blooms (HABs), primary production and physical oceanographic currents of the regional seas. Concerned with studies of HABs, satellite remote sensing proved more advantageous than any other conventional methods for large-scale applications. Probably, it may be the only source of authentic information responsible for the evaluation of new research methodologies to detect HABs. At present, studies using remote sensing methods are mostly confined to observe algal bloom occurrences, hence, it is essential to coordinate the mechanism of marine ecological and oceanographic dynamic processes of HABs using satellite remote sensing data with in situ measurements of marine environmental parameters. The satellite remote sensing on marine environment and HABs is believed to have a great improvement with popular application of technology.     

基于对抗和迁移学习的灾害天气卫星云图分类

针对卫星云图中的灾害天气数据存在严重不平衡问题,提出一个结合生成对抗学习（GAN）和迁移学习（TL）的卷积神经网络（CNN）框架以解决上述问题进而提高基于卫星云图的灾害天气分类精度。该框架主要包含基于GAN的数据均衡化模块和基于迁移学习的CNN分类模块。上述2个模块分别从数据和算法层面解决数据的类间不平衡问题,分别得到一个相对均衡的数据集和一个可在不同类别数据上提取相对均衡特征的分类模型,最终实现对卫星云图的分类,提高其中灾害天气的卫星云图类别分类准确率。与此同时所提方法在自建的大规模卫星云图数据上进行了测试,消融性和综合实验结果证明了所提数据均衡方法和迁移学习方法是有效的,且所提框架模型对各个灾害天气类别的分类精度都有显著提升。