An improved atmospheric correction algorithm for hyperspectral remotely sensed imagery

There is an increased trend toward quantitative estimation of land surface variables from hyperspectral remote sensing. One challenging issue is retrieving surface reflectance spectra from observed radiance through atmospheric correction, most methods for which are intended to correct water vapor and other absorbing gases. In this letter, methods for correcting both aerosols and water vapor are explored. We first apply the cluster matching technique developed earlier for Landsat-7 ETM+ imagery to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, then improve its aerosol estimation and incorporate a new method for estimating column water vapor content using the neural network technique. The improved algorithm is then used to correct Hyperion imagery. Case studies using AVIRIS and Hyperion images demonstrate that both the original and improved methods are very effective to remove heterogeneous atmospheric effects and recover surface reflectance spectra.     

卫星遥感大气订正的参数化模式及其模拟应用

发展了一个用于卫星大气订正的参数化模式,包括一个新的程辐射亮度模式和一个参数化的朗伯地表一大气辐射耦合引起的亮度增量模式.应用最小二乘法,程辐射亮度被参数化为大气总光学厚度、一次散射反照率、太阳天顶角、视天顶角、方位角、大气不对称因子的函数.应用这一参数化的亮度模式进行大气订正应用的数值模拟,即进行卫星遥感地表光谱反照率的模拟试验.数值检验结果表明对于865 nm,670nm,550nm和412nm 4个MODIS通道,在0°-70°的太阳天顶角、0°-60°视观测角以及0.05-0.8的地表反照率条件下,参数化的向上辐射亮度的标准差小于4%,由该参数化亮度模式引起的地表反照率解的标准差小于0.03.     

A coupled atmospheric and topographic correction algorithm for remotely sensed satellite imagery over mountainous terrain

Radiometric correction is an important issue in the quantitative remote-sensing community. By integrating dark object subtraction (DOS)-based atmospheric correction with physics-based topographic correction, a coupled land surface reflectance retrieval algorithm (coupled atmospheric and topographic correction algorithm, named the CAT algorithm) for rugged mountainous regions is proposed. Terra MODIS-derived atmospheric characterization data (including aerosol optical depth, integrated precipitable water, surface pressure, and ozone concentration) are employed as inputs for the proposed algorithm. A physics-based path radiance estimation model is proposed and embedded in the CAT algorithm, and band-specific per-pixel path radiance values are calculated. After the CAT algorithm was performed, the correlation between reflectance and terrain was dramatically reduced, with correlation coefficients nearly equal zero, especially for the near infrared and short-wave infrared bands, meanwhile the image information content increased over 20%. To provide a comparison with previous studies, two commonly used methods in the literature (DOS + Cosine and DOS + C) were employed. The results of the comparison show that the proposed algorithm performed better in both atmospheric and topographic corrections without empirical regression.     

Tessellations in GIS: Part I—putting it all together

This article attempts to describe the role of tessellated models of space within the discipline of geographic information systems (GIS)—a speciality coming largely out of geography and land surveying, where there was a strong need to represent information about the land’s surface within a computer system rather than on the original paper maps. We look at some of the basic operations in GIS, including dynamic and kinetic applications. We examine issues of topology and data structures and produce a tessellation model that may be widely applied both to traditional “object” and “field” data types. Based on this framework, it can be argued that tessellation models are fundamental to our understanding and processing of geographical space, and provide a coherent framework for understanding the “space” in which we exist. This first article examines static structures, and a subsequent article looks at “change”—what happens when things move.     

航天高光谱遥感器CHRIS的水体图像大气校正

CHRIS(Compact High Resolution Imaging Spectrometer)是欧空局于2001年10月发射的PROBA-1卫星上搭载的探索性高光谱遥感器,它具备高空间分辨率、多角度观测、高光谱成像等特点,为水质遥感监测提供不可多得的数据源。基于卫星遥感图像定量监测水质,一个关键步骤就是进行精确的大气校正,提取水面反射率。相比陆地遥感图像,水面反射率是弱信号,对大气校正的要求更高。6S(Second Simulation of SatelliteSignal in the Solar Spectrum)和MODTRAN(MOderate resolution TRANsmittance code)是两种常用的大气辐射传输模型。本文选取基于6S的REMS(Remote Sensing Environmental Monitoring System)和基于MODT-RAN的ACORN(Atmospheric CORrection Now)两种大气校正软件,对太湖梅梁湾的三景不同成像角度CHRIS图像进行大气校正,将大气校正后的图像水体反射率与地面同步实测水体反射率进行比较分析。结果表明,经过大气校正的CHRIS图像得到的水面反射率与实测反射率波形十分地接近,在全部波长范围内的相关系数达到90%。分析实测的水体反射率角度特征,发现图像的角度特征更明显。三个观测角度下反射率之间的各差值都呈现出在绿光波段较大,在红光和近红外波段偏小的特点,这和实测结果相符。ACORN校正后的图像的角度特征更好地与实测结果吻合。     

The Residuals Iteration Correction Algorithm of Exceptional Corresponding Points Based on Affine

The corresponding points are prerequisite to the registration of Terrestrial Laser Scanning data (TLS). The exceptional corresponding points will direct impact the quality of registration. The interest in this paper is in the so-called residuals iteration correction algorithm, which focused on a new procedure for correcting the exceptional corresponding points. The kernel of the procedure is the Affine proposed by Berger (1987). This paper describes the three main steps of residuals iteration correction algorithm based on Affine, namely the decomposition of exceptional corresponding points, the propagation of registration residuals, and the correction of exceptional corresponding points. The paper outlines the key advantages of the proposed approach, such as the capability to correct exceptional corresponding point automatically according to the point precision. Furthermore, it illustrates the performance of proposed approach with a validation experiment where two exceptional corresponding points were simulated and “3S” statue TLS data in Wuhan University was acquired. From the analysis of this experiment, the result shows that the validation of correction of exceptional corresponding points based on residuals iteration.     

大气校正对SPOT卫星遥测水质的影响

藉由卫星遥测进行河川水质监测，目前尚没有较明确可行之方法，如何利用较为简单且适当的SPOT卫星遥测大气校正方法，正确辨识水体水质，是本研究的主要目的。利用SPOT卫星作两阶段非监督式及监督式自动分类，确认卫星影像中水质测站对应之水体样本，并将所有样本依季节分群，俾让卫星监测水体水质样本较为均质。模拟方式采用多变量回归、类神经网络及判别分析3种模式，并比较4种不同之大气校正程序。结果发现。以水质及其指标整体预测来看，类神经网络预测结果较优于多变量回归及判别分析的结果，大气校正方法以直接采用灰度值并消除最暗像元灰度值之校正方法，即可达到不错之预测结果。综合而言，以SPOT或分辨率更高之卫星光谱遥测水质是简单可行，但仍需更多数据以验证其精确度。     

网络RTK对流层延迟内插模型精度分析

流动站与参考站间双差对流层延迟的精确改正是网络RTK技术的关键。本文采用河南省地基增强系统参考站网7个参考站的观测数据进行试验,选择LIM、LCM、LSM和KRG模型为研究对象,分析了各模型用于不同高差水平流动站对流层延迟改正的效果。试验结果表明：4种常用内插模型中,LIM和KRG模型对流动站对流层延迟改正的精度较优。流动站与主参考站高差达到400 m时,对于低高度角卫星,模型内插精度降低到分米级,不满足用户流动站高精度定位需求。     

联合统计信息与散射模型的GF-5 AHSI可见光影像薄云校正

针对高分五号可见短波红外高光谱相机AHSI (visible-shortwave infrared Advanced HyperSpectral Imager)可见光波段存在的薄云干扰,本文提出了一种联合统计信息与散射模型的校正方法。利用AHSI影像邻近波段间地表与云雾辐射的统计差异,实现对不同场景下相对薄云辐射RCR (Relative Cloud Radiance)的准确估计。基于此,根据不同波段的散射特性,分别利用分级暗目标统计和散射模型约束策略,获取可见光波段的绝对云辐射强度,最终实现影像校正。通过设置模拟与真实实验对方法的有效性和鲁棒性进行目视和定量检验。模拟实验中,可见光波段内的薄云干扰均可被有效地去除,校正结果与真实地表十分一致;此外,RMSE (RootMean-Square Error),MAE (Mean Absolute Error)和SA (Spectral Angle) 3个评价指标的值分别为1.9891,1.6822和0.4901,远小于对比方法。真实实验中,不同场景内的薄云可被有效抑制,在较为准确恢复降质地表信息的同时保持晴空区光谱特征;Q指数,SSIM (Structural Similarity Index)和UQI (Universal Quality Index)的计算结果优于对比方法。综上,本文提出方法可用于不同场景下高分五号AHSI影像可见光波段的薄云校正,可得到目视效果良好且光谱保真度高的校正结果。     

Correction of laser scanning intensity data: Data and model-driven approaches

Most airborne and terrestrial laser scanning systems additionally record the received signal intensity for each measurement. Multiple studies show the potential of this intensity value for a great variety of applications (e.g. strip adjustment, forestry, glaciology), but also state problems if using the original recorded values. Three main factors, a) spherical loss, b) topographic and c) atmospheric effects, influence the backscatter of the emitted laser power, which leads to a noticeably heterogeneous representation of the received power. This paper describes two different methods for correcting the laser scanning intensity data for these known influences resulting in a value proportional to the reflectance of the scanned surface. The first approach – data-driven correction – uses predefined homogeneous areas to empirically estimate the best parameters (least-squares adjustment) for a given global correction function accounting for all range-dependent influences. The second approach – model-driven correction – corrects each intensity independently based on the physical principle of radar systems. The evaluation of both methods, based on homogeneous reflecting areas acquired at different heights in different missions, indicates a clear reduction of intensity variation, to 1/3.5 of the original variation, and offsets between flight strips to 1/10. The presented correction methods establish a great potential for laser scanning intensity to be used for surface classification and multi-temporal analyses.     

AAC算法的大气校正复合改进算法

利用模拟数据,评价Autonomous Atmospheric Compensation(AAC)算法的抗噪性,认为AAC算法的抗噪性较弱。基于TASI实测数据,利用AAC算法开展反演计算时,计算结果呈现出多样性问题。结合In-scene Atmospheric Compensation(ISAC)算法中黑体像元的标定方法,提出了一种复合改进算法。首先,利用ISAC算法反演的大气透过率和路径辐射,重新计算AAC算法中大气透过率之比(Tr)和相邻两强弱吸收通道的路径辐射之差(Pd),再次,运用经验公式获得稳定的大气反演结果(大气透过率和路径辐射),有效解决了计算结果多样性的问题。利用复合改进算法,开展的温度与发射率分离实验,证明反演得到的发射率波谱更接近野外实测波谱。     

Analysis of Spectral Vegetation Signal Characteristics as a Function of Soil Moisture Conditions Using Hyperspectral Remote Sensing

The assessment and quantification of spatio-temporal soil characteristics and moisture patterns are important parameters in the monitoring and modeling of soil landscapes. Remote-sensing techniques can be applied to characterize and quantify soil moisture patterns, but only when dealing with bare soil. For soils with vegetation, it is only possible to quantify soil-moisture characteristics through indirect vegetation indicators, i.e. the “vitality” of plants. The “vitality” of vegetation is a sum of many indicators, whereby different stress factors can induce similar changes to the biochemical and physiological characteristics of plants. Analysis of the cause and effect of soil-moisture properties, patterns and stress factors can therefore only be carried out using an experimental approach that specifically separates the causes. The study describes an experimental approach and the results from using an imaging hyperspectral sensor AISA-EAGLE (400–970 nm) and a non-imaging spectral sensor ASD (400–2,500 nm) under controlled and comparable conditions in a laboratory to study the spectral response compared to biochemical and biophysical vegetation parameters (“vitality”) as a function of soil moisture characteristics over the entire blooming period of Ash trees. At the same time that measurements were taken from the hyperspectral sensors, the following vegetation variables were also recorded: leaf area index (LAI), chlorophyll meter value — SPAD-205, vegetation height, C/N content and leaf water content as indicators of the “vitality” and the state of the vegetation. The spectrum of each hyperspectral image was used to calculate a range of vegetation indices (VI’s) with relationships for soil moisture characteristics and stress factors. The relationship between vegetation indices and plant “vitality” indicators was analysed using a Generalized Additive Model (GAM). The results show that leaf water content is the most appropriate vegetation indicator for assessing the “vitality” of vegetation. With the Water Index (WI) it was possible to differentiate between the moisture treatments of the control, moisture drought stress and the moisture flooding treatment over the entire growing season of the plants (R ²?=?0.94). There is a correlation between the “vitality” vegetation parameters (LAI, C/N content and vegetation height) and the indicators NDVI, WI, PRI and Vog2. In our study with Ash trees the vegetation parameter chlorophyll was found not to be a suitable indicator for detecting the “vitality” of plants using the spectral indicators. There is a possibility that the sensitivity of the indicators selected was too low compared to changes in the chlorophyll content of Ash trees. Adding the co-variable ‘time’ strengthens the correlation, whereas incorporating time and moisture treatment only improves the model very slightly. This shows that changes to the biochemical and biophysical characteristics caused by phenology, overlay a differentiation of the moisture treatments.     

高光谱遥感影像多级联森林深度网络分类算法

高光谱遥感技术在环境监测、应急保障、精细地物提取等方面有着广泛的应用,随着高分五号高光谱数据的正式发布,高光谱遥感技术将发挥更重要的作用。遥感影像分类作为高光谱遥感影像信息处理的重要部分,已成为当前研究重点。本文针对传统多级联森林深度学习中模型复杂、无法利用基分类器差异信息、对类间差异较小的样本无法正确区分等不足,提出了一种改进的多级联森林深度学习模型,在模型框架中,分别采用了随机森林和旋转森林作为基分类器,并引入逻辑回归分类器作为判别器用于训练层扩展。相较于传统的深度神经网络,改进的多级联森林深度网络超参数较少且能够自适应确定训练层,更方便进行模型优化。实验采用了高分五号数据集及两个公开的高光谱数据集(Indian Pines数据集及Pavia University数据集)进行精度评定,同时选择了传统分类器支持向量机、深度置信网等模型作为对比分析。实验结果表明,改进的多级联森林深度学习模型能有效地进行高光谱遥感影像分类,且较传统的分类方法精度有所提升。     

On the determination of the effect of horizontal ionospheric gradients on ranging errors in GNSS positioning

An alternative approach to the traditionally employed method is proposed for treating the ionospheric range errors in transionospheric propagation such as for GNSS positioning or satellite-borne SAR. It enables the effects due to horizontal gradients of electron density (as well as vertical gradients) in the ionosphere to be explicitly accounted for. By contrast with many previous treatments, where the expansion of the solution for the phase advance is represented as the series in the inverse frequency powers and the main term of the expansion corresponds to the true line-of-sight distance from the transmitter to the receiver, in the alternative technique the zero-order term is the rigorous solution for a spherically layered ionosphere with any given vertical electron density profile. The first-order term represents the effects due to the horizontal gradients of the electron density of the ionosphere, and the second-order correction appears to be negligibly small for any reasonable parameters of the path of propagation and its geometry for VHF/UHF frequencies. Additionally, an “effective” spherically symmetric model of the ionosphere has been introduced, which accounts for the major contribution of the horizontal gradients of the ionosphere and provides very high accuracy in calculations of the phase advance.     

高光谱图像混合像元多维卷积网络协同分解法

受成像光谱仪性能与复杂地物分布的影响,高光谱图像存在大量的混合像元。传统的基于学习的混合像元分解方法通常都是浅层模型,或缺少对空间、光谱信息的综合应用。本文提出一种多维卷积网络协同的混合像元分解深层模型,采用多种维度卷积网络能更充分利用多种维度语义信息,有利于估计小样本和高维的高光谱图像混合像元丰度。对训练数据进行增广处理,构建光谱维、空间维和立方体3种卷积神经网络;设计了融合层,协同3种卷积神经网络提取特征,“端到端”的估计混合像元丰度值;模型使用了批量归一化、池化和Dropout方法避免过拟合现象。试验结果表明,多维卷积网络协同方法的引入能更有效地提取空-谱特征信息,与其他的卷积网络解混模型相比,估计的混合像元丰度精度有显著提高。     

特征变量选择结合SVM的耕地土壤Hg含量高光谱反演

为探讨应用高光谱数据反演耕地土壤重金属汞(Hg)含量,对原始光谱进行10 nm重采样和SG平滑处理,用不同光谱变换数据与土壤重金属Hg含量进行相关性分析,采用IRIV、Random Frog和PCC提取光谱特征波段,分别建立SVM与GWO-SVM土壤Hg含量高光谱反演模型,获取Hg含量最优反演路径。研究表明,一阶微分变换光谱后土壤光谱特征更明显;上述特征提取方法在不同程度上减少光谱数据冗余,保留有效变量信息;经灰狼算法优化后支持向量机模型反演精度提高,IRIV结合GWO-SVM预测精度更高,其验证集R²为0.894,RMSE为0.082,MAE为0.016。研究成果可为类似土壤重金属含量的反演提供借鉴。     

基于TM数据的植被覆盖度反演

本文首先对TM影像进行了几何纠正、辐射校正、大气校正;然后根据混合像元的结构特征,利用TM数据从植被指数(NDVI)中采用“等密度模型”和“非密度模型”提取了宜昌南部地区的植被覆盖度。在用“非密度模型”反演植被覆盖度的过程中,叶面积指数(LAI)是一个必要的参数,本文提出了一种改进的借助可见光波段和近红外波段反射值来提取叶面积指数(LAI)的方法。通过和MODIS数据反演结果比较表明:“非密度模型”的估算精度要高于“等密度模型”;利用“等密度模型”和“非密度模型”反演植被覆盖度是可行。     

Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs

The present study proposes land surface temperature (LST) retrieval from satellite-based thermal IR data by single channel radiative transfer algorithm using atmospheric correction parameters derived from satellite-based and in-situ data and land surface emissivity (LSE) derived by a hybrid LSE model. For example, atmospheric transmittance (τ) was derived from Terra MODIS spectral radiance in atmospheric window and absorption bands, whereas the atmospheric path radiance and sky radiance were estimated using satellite- and ground-based in-situ solar radiation, geographic location and observation conditions. The hybrid LSE model which is coupled with ground-based emissivity measurements is more versatile than the previous LSE models and yields improved emissivity values by knowledge-based approach. It uses NDVI-based and NDVI Threshold method (NDVI^THM) based algorithms and field-measured emissivity values. The model is applicable for dense vegetation cover, mixed vegetation cover, bare earth including coal mining related land surface classes. The study was conducted in a coalfield of India badly affected by coal fire for decades. In a coal fire affected coalfield, LST would provide precise temperature difference between thermally anomalous coal fire pixels and background pixels to facilitate coal fire detection and monitoring. The derived LST products of the present study were compared with radiant temperature images across some of the prominent coal fire locations in the study area by graphical means and by some standard mathematical dispersion coefficients such as coefficient of variation, coefficient of quartile deviation, coefficient of quartile deviation for 3rd quartile vs. maximum temperature, coefficient of mean deviation (about median) indicating significant increase in the temperature difference among the pixels. The average temperature slope between adjacent pixels, which increases the potential of coal fire pixel detection from background pixels, is significantly larger in the derived LST products than the corresponding radiant temperature images.     

高光谱图像相对含水量反演引导的精细分类

深度学习在高光谱图像处理领域的研究应用不断深入发展,基于深度学习的高光谱图像分类达到了较高的分类精度。目前的分类模型多利用高光谱的图谱特征,但对光谱的诊断性特征及先验信息利用不足,对空谱特征提取过程难以实现有效协同,因而导致分类类别即类内分类不够精细。为了解决以上问题,本文提出一种以多标签数据为输入的共生神经网络模型,在高光谱图谱特征提取的基础上融合光谱诊断特征,实现相对含水量反演及精细分类。首先,通过构建一种新的红边斜率光谱指数实现高光谱图像相对含水量的表征,利用本文提出的自适应分级算法完成相对含水量反演并建立对应的等级标签,与地物种类标签共同构成多标签高光谱数据集。然后,构建共生神经网络架构及内部变维特征提取模块,利用多标签数据提取高光谱图像中空间、光谱和相对含水量的融合特征,提高深度模型对不同含水量地物的区分能力和对所提取特征的协同表达能力,降低模型的复杂度与计算量,完成基于多标签数据集的相对含水量反演引导分类的过程,在扩大传统类间距离的基础上进一步扩大类内距离,从而实现高光谱图像的精细分类。最后,使用实验室采集数据和4个公开的高光谱数据集Lopex、Indian Pines、Pavia University和Salinas进行实验验证。结果表明,本文提出的红边斜率光谱指数可以有效表征地物的相对含水量信息;相对含水量反演引导的分类模型对类内分类精度有较明显的提升,对总体分类结果有一定的改善;与其他机器学习和深度学习分类算法相比,本文算法取得了较好的分类结果,提高了深度分类模型的分类性能和精细程度,实现了精细分类。     

Tessellations in GIS: Part II–making changes

Abstract

We attempt to describe the role of tessellated models of space within the discipline of Geographic Information Systems (GIS) – a speciality coming largely out of Geography and Land Surveying, where there was a strong need to represent information about the land’s surface within a computer system rather than on the original paper maps. We look at some of the basic operations in GIS, including dynamic and kinetic applications. We examine issues of topology and data structures, and produced a tessellation model that may be widely applied both to traditional “object” and “field” data types. The Part I of this study examined object and field spatial models, the Voronoi extension of objects, and the graphs that express the resulting adjacencies. The required data structures were also briefly described, along with 2D and 3D structures and hierarchical indexing. The importance of graph duality was emphasized. Here, this second paper builds on the structures described in the first, and examines how these may be modified: change may often be associated with either viewpoint or time. Incremental algorithms permit additional point insertion, and applications involving the addition of skeleton points, for map scanning, contour enrichment or watershed delineation and simulation. Dynamic algorithms permit skeleton smoothing, and higher order Voronoi diagram applications, including Sibson interpolation. Kinetic algorithms allow collision detection applications, free-Lagrange flow modeling, and pen movement simulation for map drawing. If desired these methods may be extended to 3D. Based on this framework, it can be argued that tessellation models are fundamental to our understanding and processing of geographical space, and provide a coherent framework for understanding the “space” in which we exist.