SCS+C地形辐射校正模型的应用分析研究

在对有森林覆盖的山区影像进行地形辐射校正时，基于太阳-冠层-传感器（SCS）几何关系的校正模型优于基于太阳-地形-传感器（STS）几何关系的模型。SCS校正模型解释了树木不依赖于地形、观测角和光照入射角而具有向地性生长的本质特性，但在某些地形区域，SCS与余弦校正同样存在过度校正的问题。为了解决这个问题，研究者在SCS校正模型中引入C校正系数来解释散射辐射项，提出了SCS+C校正模型。以北京密云Landsat 5影像为数据源，通过目视判别、直方图、定量的统计参数和地物光谱曲线对比等方法，对SCS+C校正模型与传统的余弦校正、C校正和SCS校正模型进行了对比。结果表明，4种方法均能在很大程度上消除地形阴影，更好地反映阴影区域的细节信息； 从总体的光谱特性保真程度来说，余弦和SCS校正都因过度校正问题表现较差，SCS+C校正最好，C校正次之。     

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。     

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。     

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。     

地形辐射校正模型研究进展

从经验模型、物理模型及半经验模型3个方面综述了地形辐射校正模型研究的主要进展,并从模型的输入参数、前提假设条件和评价方法3个方面讨论了现有模型存在的一些问题.最后,对地形辐射校正模型的研究前景进行了展望,认为可以从多源或多时相数据、图像增强或信息填充等方面恢复阴影区域信息,提高模型的校正精度,考虑引入新的数理统计方法进行精确的定量评价,并建议地形辐射校正模型研究应该更加重视阴影区域光谱信息的恢复,尤其是在地形复杂的山区.     

邻坡反射辐射对地物反射率计算的影响评估

采用朗伯体经验模型计算邻坡反射辐射,并建立其对目标像元反射率计算影响的评估模型。研究表明,忽略邻坡反射辐射将造成目标像元反射率的计算值偏高,尤其在低照度区域易导致光学遥感影像地形辐射校正出现过饱和现象。研究证实,雪的可见光近红外波段的邻坡反射辐射、植被的近红外波段的邻坡反射辐射对目标像元反射率计算的影响显著,对地形遮蔽度大的目标像元可产生10%以上的反射率相对误差。     

合成孔径雷达影像地形校正半经验模型

结合SAR成像特点和数学理论知识,给出左视、右视两种侧视成像情况下影响地形起伏区域SAR后向散射的本地入射角理论计算模型,基于微波散射物理模型AIEM,模拟不同雷达入射角下地形坡度、坡向对SAR数据后向散射的影响,结果表明雷达入射角相对较小的SAR数据受地形起伏影响较小,是地形起伏地区SAR应用的最佳数据源。并提出一种SAR影像后向散射系数的地形校正半经验模型。地形校正过的SAR影像分类总体精度较未校正SAR影像提高12%。     

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FLAASH和ATCOR2模型的Landsat ETM+影像大气校正比较

利用FLAASH和ATCOR2模型对漓江流域的Landsat ETM+数据进行大气校正,以GLS(Global Land Survey)获得的同步高质量地表反射率影像作为参考数据,从目视效果、典型地物光谱特征和波谱一致性三方面对两种模型的校正结果进行对比分析。研究表明,两种模型均可以对ETM+影像进行有效的大气校正,FLAASH模型的校正精度优于ATCOR2模型。     

一种基于彩色模型的遥感影像阴影检测方法

依据遥感影像阴影的属性,提出一种基于彩色模型的遥感影像阴影检测方法,以提高阴影检测精度。阴影检测过程中,首先将影像转换到HSV空间, 根据阴影区域亮度值低和饱和度高的特性, 新定义M=（S-V）/(H+S+V),并结合小区域去除和数学形态学处理,提取阴影区域;其次依据散射理论对蓝光的影响,提出结合C1C2C3空间的C3分量和RGB空间的B分量进行双阈值阴影检测;为降低阈值选择的主观性,提出将上述两种方法进行与运算进行阴影提取。最后对多幅带有阴影的遥感影像进行实验,结果表明所提出的方法明显优于传统的直方图阈值法和形态学检测法,克服了阈值选择的主观性,提高了阴影检测精度。     

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.     

Evaluation of different topographic correction methods for Landsat imagery

The recent free availability of Landsat historical data provides new potentials for land-cover change studies. Multi-temporal studies require a previous radiometric and geometric homogenization of input images, to better identify true changes. Topographic normalization is one of the key steps to create consistent and radiometricly stable multi-temporal time series, since terrain shadows change throughout time. This paper aims to evaluate different methods for topographic correction of Landsat TM-ETM+ data. They were assessed for 15 ETM+ images taken under different illumination conditions, using two criteria: (a) reduction of the standard deviation (SD) for different land-covers and (b) increase in temporal stability of a time series for individual pixels. We observed that results improve when land-cover classes where processed independently when applying the more advanced correction algorithms such as the C-correction and the Minnaert correction. Best results were obtaining for the C-correction and the empiric–statistic correction. Decreases of the SD for bare soil pixels were larger than 100% for the C-correction and the empiric–statistic correction method compared to the other correction methods in the visible spectrum and larger than 50% in the IR region. In almost all tests the empiric–statistic method provided better results than the C-correction. When analyzing the multi-temporal stability, pixels under bad illumination conditions (northern orientation) improved after correction, while a deterioration was observed for pixels under good illumination conditions (southern orientation). Taken this observation into account, a simple but robust method for topographic correction of Landsat imagery is proposed.     

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°.     

山地叶面积指数反演理论、方法与研究进展

叶面积指数LAI（Leaf Area Index）是表征叶片疏密程度和冠层结构特征的重要植被参数，在气候变化、作物生长模型以及碳、水循环研究中发挥着重要作用。遥感是获取区域及全球尺度LAI的一个重要手段，当前LAI产品主要基于遥感数据反演得到，但是多数LAI产品算法并未考虑地形特征的影响，导致山地LAI遥感反演精度不确定性大。提高山地LAI遥感反演精度亟需考虑地形因子对冠层反射率的影响，其中山地冠层反射率模型和遥感数据地形校正是提升山地LAI遥感反演精度的关键。本文围绕山地LAI遥感反演理论与方法，综合分析了国内外山地冠层反射率模型和地形校正模型的研究进展，总结了目前山地LAI遥感反演存在的问题，并讨论了未来研究的发展趋势。     

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。     

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.     

Issues in the application of Digital Surface Model data to correct the terrain illumination effects in Landsat images

The accuracy of topographic correction of Landsat data based on a Digital Surface Model (DSM) depends on the quality, scale and spatial resolution of the DSM data used and the co-registration between the DSM and the satellite image. A physics-based bidirectional reflectance distribution function (BRDF) and atmospheric correction model in conjunction with a 1-second DSM was used to conduct the analysis in this paper. The results show that for the examples used from Australia, the 1-second DSM, can provide an effective product for this task. However, it was found that some remaining artefacts in the DSM data, originally due to radar shadow, can still cause significant local errors in the correction. Where they occur, false shadows and over-corrected surface reflectance factors can be observed. More generally, accurate co-registration between satellite images and DSM data was found to be critical for effective correction. Mis-registration by one or two pixels could lead to large errors of retrieved surface reflectance factors in gully and ridge areas. Using low-resolution DSM data in conjunction with high-resolution satellite images will also fail to correct significant terrain components where they occur at the finer scales of the satellite images. DSM resolution appropriate to the resolution of satellite image and the roughness of the terrain is needed for effective results, and the rougher the terrain, the more critical will be the accurate registration.     

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。