陡峭山区影像的半经验地形校正

本文提出一种新的半经验地形校正模型SCEDIL(Simple topographic Correction using Estimation of Diffuse Light),该模型通过结合DEM与光学影像数据寻找局部区域内完全光照和阴影的水平像元,并以光照、阴影水平像元的平均反射率值估算局部区域散射辐射比,提高了陡峭山区影像的地形校正精度。以高分一号卫星和Landsat ETM+影像为例,从目视判读和定量分析两个方面,比较分析该算法与传统半经验地形校正算法(C、SCS+C)的校正结果。结果表明:(1)对较为平坦的地形,SCEDIL和C、SCS+C校正都有较好的目视结果;对地面起伏较大的陡峭地形,C、SCS+C校正后,原阴影区域易呈现破碎化特征,SCEDIL校正后,原阴影区域过渡较为平滑。(2)SCEDIL校正后,各波段反射率的均值和标准差优于C、SCS+C校正,SCEDIL校正后,影像总分类精度与同类地物光谱信息均一性均优于C和SCS+C校正。SCEDIL半经验地形校正方法能有效地去除影像中的地形干扰,尤其对陡峭地形的校正效果,优于常规地形校正模型。     

考虑地物类型的Minnaert地形校正模型优化方法EI北大核心CSCD

地形校正可以削弱地势复杂区域由于地形起伏导致的地表接收太阳辐射不均匀和地表反射率失真的问题,从而提升遥感影像质量和遥感信息提取的精度。但是,现有地形校正模型存在过校正、波段间校正效果不稳定以及校正效果不理想等问题。本文根据Minnaert地形校正模型系数k和地物二向性反射特性的相关性,对Minnaert模型进行改进,提出了一种考虑地物类型的Minnaert地形校正模型(简称为CMinnaert模型),并在地物预分类中采用《土地利用现状分类》一级分类标准和分植被疏密程度分类两种方式,用以验证CMinnaert模型的稳定性并筛选最佳地物类型划分方案。首先对待校正影像进行地物类型预分类,其次逐波段针对各地物类型分别进行系数k的拟合求解,然后使用各波段各地物类型的系数k对该范围的遥感影像进行Minnaert地形校正。以河南省商城县的Landsat 8/OLI影像为实验数据,选取余弦校正模型、SCS校正模型、Minnaert校正模型、分坡度的Minnaert校正模型作为对比模型,通过目视对比和统计数据分析的方式评估CMinnaert模型的地形校正效果。研究结果表明,本文提出的CMinnaert模型有效地削弱了地形效应对遥感影像辐射亮度值的影响,与原始影像和其他4种地形校正结果相比,进行地物一级分类的CMinnaert模型有效降低了各波段辐亮度与太阳入射角余弦的线性拟合R2,未出现过校正现象;分植被疏密程度分类的CMinnaert模型在第1、5波段存在过校正问题,但其余波段辐亮度与太阳入射角余弦的线性拟合R2是6种模型中最低的。以上结果证明两种地物预分类方式的CMinnaert模型校正效果都较稳定且明显优于其他四种地形校正模型,且本文建议在进行CMinnaert地形校正时采用地物一级分类的方式进行地物预分类。     

基于Three Factor+C模型改进的地形辐射校正方法

地形辐射校正(简称"地形校正")是复杂地形遥感定量化研究的关键环节之一。针对传统的经验地形校正模型存在的不同坡度采用同一校正系数的缺陷,基于简化的Three Factor+C模型,借鉴改进型Minnaert模型中坡度分级的思想,提出了基于Three Factor+C+坡度的地形校正方法。结果表明,使用Three Factor+C+坡度模型进行地形校正后的遥感图像,其均值、标准差、像元值与光照系数的相关性、阴阳坡光谱辐亮度值、离散指数和同质系数等6个指标均优于参与比较的C模型、SCS模型、Three Factor模型和Three Factor+C模型的对应指标。Three Factor+C+坡度模型有比较完善的物理机制,并较好地消除地形对光谱辐亮度的影响,值得推广。     

地形抹平与半经验模型的Landsat TM/ETM+地形校正方法

地形校正可以减小地形起伏对地物光谱的影响,提高计算机分类在山区的精度。设计了针对全球土地覆盖分类的Landsat TM/ETM+数据地形校正方法 SCOS(Smoothed COS余弦),首先对地形的坡度角进行抹平处理,很大程度上削弱了地表非朗伯性对地形校正的影响,然后利用简单有效的余弦校正去除地形效应。该方法与其他常用地形校正算法的对比分析是通过对全球不同区域、不同地表覆盖的有代表性的6景Landsat TM/ETM+数据的试验,采用统计分析与目视判读的方式,从过度校正和类内均一性两个方面进行的。结果表明,该方法在目视效果和统计结果上优于常规方法,并且更加简单有效,无需复杂的大气参数及传感器参数,满足全球地表覆盖分类对地形校正的需求。     

考虑植被覆盖因子的地形辐射校正模型

针对传统的地形辐射校正模型无法适用复杂地表覆盖类型而导致的校正精度较低的问题,该文提出了一种考虑像元植被覆盖因子的模型。山区遥感影像像元大部分为植被与岩石、裸土的混合像元,针对混合像元中岩石、裸土部分应用太阳-地表-传感器模型,而对于植被覆盖区则采用考虑植被垂直生长特性的太阳-树冠-传感器模型,两模型用像元植被覆盖因子拟合为新的太阳-植被覆盖因子-传感器模型。利用覆盖江西实验区的Landsat-8陆地成像仪影像和数字高程模型数据进行了校正比对分析,结果表明该方法可有效地消除地形起伏对辐射亮度的影响。     

DEM辅助的卫星光学遥感影像山体阴影检测与地形辐射校正

提出一种结合DEM的山体阴影检测与地形辐射校正方法。首先对卫星影像多波段信息用特征法进行阴影检测,然后结合DEM数据用模型法进行山体背阴面检测以及投影区域检测,将3个结果综合分析,按照形成原因将阴影检测结果分为8类,最后结合太阳入射角信息,利用信息匹配的阴影补偿法和地形辐射校正物理模型,进行卫星光学遥感影像辐射校正。试验证明该方法能恢复山体阴影区的信息,并且有效降低地形效应的影响。     

基于Radarsat-2数据的星载SAR正射校正和地形辐射校正方法研究

地形对雷达影像的几何和辐射特性都有强烈的影响.对雷达影像进行定量分析和参数提取之前,必须对SAR影像进行精确的几何校正和辐射校正,消除地形的影响.基于RD定位模型和数字高程模型建立一种正射校正和地形辐射校正(TRC)方法.通过试验,从定性和定量两方面评价正射校正和地形辐射校正结果的有效性.比较基于投影角和基于局部入射角...     

一种改进的遥感影像地形校正方法

地形校正是遥感影像定量化应用环节之一,以往的地形校正研究多是针对一景影像中很小的局部影像块来进行处理研究的,对整景大场景影像进行地形校正的研究尚不多。基于此,本文利用高分一号的宽视场相机拍摄的16 m分辨率的遥感影像,研究了大场景下地形校正方法,对C校正模型进行了改进,在C校正模型中加入了反射角的影响,并且验证了改进模型的合理性;最后对改进的模型与余弦校正模型、传统的C校正模型的处理结果进行了比较。通过分析,利用改进的模型,影像的标准差普遍变小,影像校正后的阴阳坡亮度值趋于一致的趋势更明显。试验结果表明,对大场景、非星下点成像的遥感影像利用改进模型进行地形校正效果明显增强。     

包络线去除的丘陵地区遥感影像阴影信息重建

中国西南丘陵常态山和喀斯特山交错分布,遥感影像普遍存在山体阴影,分布零散且无规律,基于DEM的地形校正模型(C校正等)虽然算法成熟、易于操作,但在复杂地形区存在误差。引入基于相似像元包络线的阴影校正方法(CR校正),按照阴影提取、包络线去除、相似像元寻找和阴影亮度重建的步骤,采用西南丘陵地区Landsat 8 OLI影像进行验证实验。结果表明:CR校正后,阴影区的视觉特征与邻近非阴影区趋于一致,阴影像元亮度有明显提升;校正后影像主要波段标准差减小,与非阴影区参考光谱的相对均方根误差在2.919%以内,最低仅为0.516%;自动分类精度从43.59%提高到61.57%,CR校正有效提高了有阴影的丘陵地区遥感影像质量。     

遥感影像地形校正物理模型的简化与改进

针对考虑太阳直接辐射、天空散射辐射以及来自于附近地表反射辐射的复杂地形EMT 遥感影像地形校正物理模型公式复杂、计算繁琐、不易实施的特点以及存在过度校正的缺陷,对该物理模型进行简化和改进。提出一套简化模型相关参数,即r值,Vt,Vd,T↓(λ,θ)等的计算方案,从而简化计算过程,提高计算效率。并针对该地形校正物理模型朗伯体假设的缺陷,引入Minnaert参数k时模型进行非朗伯体修正。简化和改进的地形校正物理模型的校正实验结果表明该模型很好地消除了复杂地形EMT 遥感影像的地形阴影,从而证明该地形校正物理模型的简化和改进方案可行。     

Assessment of different topographic correction methods in ALOS AVNIR-2 data over a forest area

Abstract

Because the removal of topographic effects is one the most important pre-processing steps when extracting information from satellite images in digital Earth applications, the problem of differential terrain illumination on satellite imagery has been investigated for at least 20 years. As there is no superior topographic correction method applicable to all areas and all images, a comparison of topographic normalization methods in different regions and images is necessary. In this study, common topographic correction methods were applied on an ALOS AVNIR-2 image of a rugged forest area, and the results were evaluated through different criteria. The results show that the simple correction methods [Cosine, Sun-Canopy-sensor (SCS), and Minnaert correction] are inefficient in exceptionally rough forests. Among the improved correction methods (SCS+C, modified Minnaert, and pixel-based Minnaert), the best result was achieved using a pixel-based Minnaert approach in which a separate correction factor in various slope angles is used. Thus, this method should be considered for topographic correction, especially in forests with severe topography.     

光学遥感影像表观辐亮度地形效应纠正物理模型

针对已有地形纠正方法的不足, 在山区辐射传输模型简化的基础上, 提出了水平地面上接收到的漫射辐射与垂直于太阳方向表面接收的直射辐射比例因子的概念, 建立了仅需要太阳角度信息和大气模式作为输入参数, 主要针对地形效应本身进行纠正的简单纠正模型, 可以将复杂地形区光学遥感影像表观辐亮度纠正为无地形影响的水平地表辐亮度, 并以TM影像为例进行了实验验证。     

An improved topographic correction model based on Minnaert

The uneven distribution of solar radiation due to topographic relief can significantly change the correlation between reflectance and other features such as biomass in rugged terrain regions. In this article, we use the transfer theory to improve the Minnaert approach. After comparing topographic correction methods for Landsat 8 Operational Land Imager (OLI) and EO-1 Advanced Land Imager (ALI) imagery acquired from the mountainous region in Beijing, China, we used visual inspection, statistical analysis, and correlation analysis to evaluate the algorithms and performance of the proposed Minnaert-E approach. The results indicate that corrections based on non-Lambertian methods have better performance than those based on the Lambertian assumption. The correction performances can be ranked as the Minnaert-E, followed by the Minnaert and the SCS+C corrections, and, finally, the C-HuangWei correction, which performed the worst. We found that the Minnaert-E approach can effectively weaken the influence of terrain relief on pixels and restore the true reflectance of the pixels in the relief area. Further analysis indicates that the Minnaert-E has a better effect on image processing where the slope gradient is restricted to less than 10° or between 30° and 43°.     

A sun–crown–sensor model and adapted C-correction logic for topographic correction of high resolution forest imagery

Canopy shadowing mediated by topography is an important source of radiometric distortion on remote sensing images of rugged terrain. Topographic correction based on the sun–canopy–sensor (SCS) model significantly improved over those based on the sun–terrain–sensor (STS) model for surfaces with high forest canopy cover, because the SCS model considers and preserves the geotropic nature of trees. The SCS model accounts for sub-pixel canopy shadowing effects and normalizes the sunlit canopy area within a pixel. However, it does not account for mutual shadowing between neighboring pixels. Pixel-to-pixel shadowing is especially apparent for fine resolution satellite images in which individual tree crowns are resolved. This paper proposes a new topographic correction model: the sun–crown–sensor (SCnS) model based on high-resolution satellite imagery (IKONOS) and high-precision LiDAR digital elevation model. An improvement on the C-correction logic with a radiance partitioning method to address the effects of diffuse irradiance is also introduced (SCnS + C). In addition, we incorporate a weighting variable, based on pixel shadow fraction, on the direct and diffuse radiance portions to enhance the retrieval of at-sensor radiance and reflectance of highly shadowed tree pixels and form another variety of SCnS model (SCnS + W). Model evaluation with IKONOS test data showed that the new SCnS model outperformed the STS and SCS models in quantifying the correlation between terrain-regulated illumination factor and at-sensor radiance. Our adapted C-correction logic based on the sun–crown–sensor geometry and radiance partitioning better represented the general additive effects of diffuse radiation than C parameters derived from the STS or SCS models. The weighting factor W_t also significantly enhanced correction results by reducing within-class standard deviation and balancing the mean pixel radiance between sunlit and shaded slopes. We analyzed these improvements with model comparison on the red and near infrared bands. The advantages of SCnS + C and SCnS + W on both bands are expected to facilitate forest classification and change detection applications.     

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.     

A shadow- eliminated vegetation index (SEVI) for removal of self and cast shadow effects on vegetation in rugged terrains

ABSTRACT

The effect of terrain shadow, including the self and cast shadows, is one of the main obstacles for accurate retrieval of vegetation parameters by remote sensing in rugged terrains. A shadow- eliminated vegetation index (SEVI) was developed, which was computed from only red and near-infrared top-of-atmosphere reflectance without other heterogeneous data and topographic correction. After introduction of the conceptual model and feature analysis of conventional wavebands, the SEVI was constructed by ratio vegetation index (RVI), shadow vegetation index (SVI) and adjustment factor (f (Δ)). Then three methods were used to validate the SEVI accuracy in elimination of terrain shadow effects, including relative error analysis, correlation analysis between the cosine of solar incidence angle (cosi) and vegetation indices, and comparison analysis between SEVI and conventional vegetation indices with topographic correction. The validation results based on 532 samples showed that the SEVI relative errors for self and cast shadows were 4.32% and 1.51% respectively. The coefficient of determination between cosi and SEVI was only 0.032 and the coefficient of variation (std/mean) for SEVI was 12.59%. The results indicate that the proposed SEVI effectively eliminated the effect of terrain shadows and achieved similar or better results than conventional vegetation indices with topographic correction.     

Impact of Topography on Accuracy of Land Cover Spectral Change Vector Analysis Using AWiFS in Western Himalaya

The present paper discusses the impact of topography on accuracy for land cover classification and “from-to class change using improved spectral change vector analysis suggested by Chen et al. (2003). Two AWiFS sensor images of different dates are used. Double Window Flexible Pace Search (DFPS) is used to estimate threshold of change magnitude for change/no change classes. The topographic corrections show accuracy of 90% (Kappa coefficient 0.7811) for change/no change area as compared to 82% (Kappa coefficient 0.6512) in uncorrected satellite data. Direction cosines of change vector for determining change direction in n-dimensional spectral space is used for image classification with a minimum distance categorizing technique. The results of change detection are compared (i) Improved CVA with conventional two bands CVA and (ii) Improved CVA before and after topographic corrections. The improved CVA with topographic correction consideration using slope match show maximum accuracy of 90% (Kappa coefficient 0.83) as compared to conventional CVA which show maximum accuracy of 82% (Kappa coefficient 0.6624). The overall accuracy of ”from- to class using improved CVA increases from 86% (Kappa coefficient 0.7817) to 90% (Kappa coefficient 0.83) after topographic corrections. The improved CVA with proper topographic corrections is found to be effective for change detection analysis in the rugged Western Himalayan terrain.     

地表反射率地形校正物理模型与效果评价方法研究进展

地形校正是准确获取地形复杂区遥感反射率的重要步骤，对提高山区地表遥感参数定量化反演精度，扩大遥感产品应用广度具有重要意义。从20世纪80年代开始，国内外学者开始对准确获取山区地表遥感反射率进行研究，建立了多种地形校正模型来减少或消除遥感图像中地形效应影响，减少同种地表类型的反射率差异，并将地形校正模型分为经验模型和物理模型。根据构建物理模型时是否考虑地表非朗伯体特性，将物理模型分为朗伯体假设模型和非朗伯体假设模型，本文分别从朗伯体假设模型和非朗伯体假设校正模型展开叙述。从两类模型构建的理论基础，模型特点，局限性等几方面进行分析和讨论，描述了两类模型的发展历程，系统阐述了朗伯体假设模型和非朗伯体假设模型的适用性和不足，剖析了目前地形校正模型存在的问题与挑战。同时，本文也比较了应用于地形校正的效果评价方法，并展望了地形校正方法和地形校正评价方法的未来主要发展方向。