基于不同地形校正模型的影像反射率对比分析

以甘肃省兰州市东南部的兴隆山地区为例,使用Landsat 8 OLI遥感影像,对研究区影像的各个波段分别进行余弦校正、C校正和改进型Minnaert校正,通过实地测量和对比影像的统计特征两种方法,对校正结果进行对比分析。结果发现:在可见光和短波红外波段,余弦模型的校正效果比较理想,C模型存在过度校正现象;而在近红外波段,使用C模型能够得到比较好的校正结果,余弦模型的过校正现象严重;从7个波段的整体校正效果来看,改进型Minnaert模型的校正效果较为理想。从校正前后不同地形特征的反射率变化可以看出,几种模型在小坡度的校正效果基本近似;模型的过校正现象主要发生在阴坡地区,而且随着坡度的增加,阴坡的过校正现象更加明显;改进型Minnaert模型的整体过校正现象较少,比较适合在坡度较大、地形较复杂地区的校正。     

一种改进的卫星影像地形校正算法

地形校正的目的主要是补偿由于不规则的地形起伏而造成的地物亮度的变化。由于这种变化会导致相似或同种植被的反射率不一致而影响遥感影像的分类精度,因此,精确的地形校正不仅能提高影像分类的精度,而且还是遥感应用的前提。C校正方法是常用的一种基于影像像素值和太阳入射角余弦值之间线性关系的经验校正方法,但它必须对遥感影像的每一波段都进行像素值和入射角余弦值的线性拟合,这是一个复杂费时的过程。为了改善校正效果和节省校正时间,提出了一种改进的C校正算法,即在不进行线性拟合的情况下也可以达到良好的校正效果。利用三峡地区的TM影像和DEM数据所做的实验证明,该改进方法对影像进行地形辐射校正的结果比原始的C校正算法以及余弦校正都有较大的提高,且优于C校正模型。     

CIVCO地形校正模型的改进及其应用

在地形复杂的山区,地形效应造成了地物的光谱特征扭曲,严重地影响了地物的识别与分类。地形校正不仅能提高遥感识别与分类的精度,还是各种定量化遥感应用的前提。CIVCO模型是一种基于统计的经验地形校正模型,原模型中阴坡和阳坡需要手工选取,且校正效果随坡度而异。通过利用太阳方位角和坡向简化了阴坡和阳坡的选取,并按照坡度分级,分别求出校正系数。在模型改进的基础上,选择黑河上游祁连山区的TM影像和相应的DEM数据进行试验,结果表明,与C模型及原模型相比,改进的模型取得了更好的校正效果。     

遥感影像地形与大气校正系统设计与实现

遥感影像地形和大气校正是提高定量化遥感数据处理精度的重要因素。目前的数据处理软件系统集成了一些地形和大气校正算法,但在应用中还存在不能获取重要的地形参数(如阴影因子、天空可视因子等),需提供精准DEM和校正方法基于朗伯体地表假设等问题。为应对遥感专业用户需求,设计并实现了遥感影像地形与大气校正软件系统,用以对影像进行地形辐射校正、获取DEM数据和相关地形参数、地形与大气校正等。介绍了系统的功能模块设计并展示了系统的原型版本,并应用系统中的地形和大气校正方法获取了HJ/CCD影像和Landsat/TM影像的反射率。校正结果表明:该系统中的BRDF模型能够有效消除地形影响。系统的实现可以为遥感科学研究和应用提供支撑。     

光学遥感的山地像元反射率反演模型研究

基于辐射传输理论,结合Minnaert BRDF地形校正,以消除地形起伏对像元方向反射率的影响,进而发展了一种山地像元反射率精确反演模型.以密云水库地区为例,结合ETM遥感影像对该模型进行了实验验证,研究结果表明,通过该模型反演的山地像元反射率精确稳定,显著消除了地形的影响.     

基于IDL的遥感影像大气与地形校正方法实现

介绍了利用交互式数据语言（Interactive Data Language,IDL）开发TM/ETM遥感影像大气与地形校正模型的详细过程,以2000年4月30日密云ETM影像为例,对大气与地形校正方法的有效性和实用性进行了验证。结果表明,该方法有效地消除了大气与地形影响,提高了地表反射率等地表参数的反演精度和数据质量,为进一步开展定量遥感研究提供了数据质量保障。     

基于多源遥感数据的积雪反照率反演研究

积雪反照率在全球气候系统中的作用显著。由于目前遥感手段的限制,积雪反照率遥感产品存在显著的数据缺失和误差不确定性。研究对遥感积雪反照率反演模型进行精度评估,开展以渐进辐射传输理论(ART)为代表的积雪反照率遥感反演算法验证工作,分别比较MODIS、TM/ETM+数据在反演积雪反照率时的差异和准确性。结果表明:利用ART模型对积雪反射率进行各向异性校正后反演得到的积雪反照率精度优于MOD10A1积雪反照率;高分辨率遥感影像在反演积雪反照率时精度明显高于低分辨率遥感影像;针对地形复杂的高寒山区,尺度效应对积雪反照率的反演会产生极大影响。     

TM遥感影像的地形辐射校正研究

从地面所接收到的太阳直接辐射、天空散射辐射和临近地形反射附加的辐射三个方面分析计算地面每个像元的太阳总辐射,并在此基础上建立地表真实反射率恢复模型,实现对地形的辐射校正。在算法实现上,采用交互式数据语言（Interactive Data Language,IDL）,结合6S大气校正模型和数字高程模型（DEM）进行编程实现。利用北京山区的TM遥感影像所做的实验表明该方法能有效地消除卫星影像中地形的影响,为影像的后续处理提供更真实的信息。     

机载AISA Eagle Ⅱ高光谱数据处理——以额济纳旗试验区为例

机载AISA EagleⅡ传感器为"黑河综合遥感联合试验(HiWATER)"额济纳旗试验区提供航空高光谱影像。介绍了高光谱原始数据的辐射定标、几何校正、大气校正等预处理过程。根据研究区地形差异以及数据使用目的的多样性,几何校正中可选择是否加高精度DEM产品,大气校正的选择策略可分为平坦地形无DEM的大气校正和起伏地形添加DEM大气校正。本试验数据采用加载高精度DEM的几何校正和平坦地形大气校正方法,经过预处理后的高光谱数据产品,其地理坐标与高分辨率的CCD影像对比,地理位置信息较为准确;与实测地物光谱对比,影像光谱能较好地体现地物光谱的特性,数据可用作定量遥感进一步的研究。     

RS和GIS支持下的山区土地覆盖分类研究--以重庆市酉阳县为例

详细论述了使用TM卫星遥感影像提取土地覆盖信息的技术与方法。针对山区地形复杂、常年多云雾、分粪难度大的特点。着重加强对影像的地形校正和去云处理,以此来提高图像分类精度。研究结果表明,这种方法能有效地提取喇卫星遥感土地覆盖信息,特别适合我国南方山区土地覆盖信息的提取。     

A simple empirical topographic correction method for ETM+ imagery

A simple topographic correction approach, the Variable Empirical Coefficient Algorithm (VECA), was developed using theoretical and statistic analyses of the radiance values of remotely sensed data acquired for rugged terrain and the cosine of the solar illumination angle (cos i). Visual comparison and statistical analysis were used for evaluation of the proposed algorithm and the performance of the VECA approach was compared with 10 commonly used methods. The test site selected for this study is located on the south hill of the Qinling Mountain in Shanxi province, China, and the remotely sensed data used were from Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) images. The results indicate that the Cosine‐T, Cosine‐C, sun–canopy–senor (SCS) and Cosine‐b correction have the problem of overcorrection, and the other corrections can be classed into three ranks: the VECA, b correction and C models performed the best, followed by the Teillet‐regression correction model, and the SCS+C, Minnaert and Minnaert‐SCS corrections performed the worst. The proposed VECA correction and the b correction are the most capable of removing the topographic effects of the ETM+ image. The VECA is not only simple in theory but also easy to operate, indicating that the VECA is an effective topographic correction tool in remote sensing techniques.     

Evaluating and comparing performances of topographic correction methods based on multi-source DEMs and Landsat-8 OLI data

Topographic correction is a crucial and challenging step in interpreting optical remote-sensing images of extremely complex terrain environments due to the lack of universally suitable correction algorithms and digital elevation models (DEMs) of adequate resolution and quality. The free availability of open source global DEMs provides an unprecedented opportunity to remove topographic effects associated with remote-sensing data in remote and rugged mountain terrains. This study evaluated the performances of seven topographic correction methods including C-correction (C), Cosine C-correction (CC), Minnaert correction (M), Sun–canopy–sensor (SCS) correction (S), SCS+C-correction (SC), Teillet regression correction (TR), and the Terrain illumination correction model (TI) based on multi-source DEMs (local topographic map, Shuttle Radar Topography Mission (SRTM) DEM filled-finished A/B and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) data sets) and Landsat-8 Operational Land Imager (OLI) data using visual and statistical evaluation strategies. Overall, these investigated topographic correction methods removed topographic effects associated with Landsat-8 OLI data to varying degrees. However, the performances of these methods generally depend on the use of different DEMs and evaluation strategies. Among these correction methods, the SCS+C-correction performed best and was less sensitive to the use of different DEMs. The performances of topographic corrections based on free and open-access DEMs were generally better than or comparable to those based on local topographic maps. In particular, the topographic correction performance was greatly improved using the SRTM filled-finished B (FFB) data set with a resampling scheme based on the average value within a 3 × 3 pixel window. Nevertheless, further quantitative investigation is needed to determine the relative importance of DEMs and evaluation strategies used to select topographic correction methods.     

Topographic correction algorithm for remotely sensed data accounting for indirect irradiance

The solar irradiance incidents upon terrain surface are composed of three parts, i.e. direct solar irradiance, diffuse sky irradiance and reflected irradiance from the adjacent surface, respectively. Most of the topographic correction models only account for the topographic effect induced from direct solar irradiance, and few models take the topographic effects from the last two components of solar irradiance into account. A physically based topographic correction algorithm aiming to overcome this shortcoming, called a three-factor correction model, was developed based on theoretical analysis of radiation transferring processes along an undulating surface, atmosphere and satellite sensor geometry under the assumption of Lambertian surface. On the basis of this three-factor correction model, an advanced algorithm accounting for the bidirectional reflectance distribution function (BRDF) nature of non-Lambertian surface, called the three-factor+C topographic correction model, was developed by introducing an empirical parameter C to approximate the indirect irradiance contribution of non-Lambertian surface. Performances of these two newly developed algorithms were tested and compared with those of Cosine and C correction algorithms for a selected rugged terrain on the south flank of the Qinling Mountain, China. Visual comparison and statistical analysis were adopted for quantitative evaluation on topographic corrections of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image in the study. The results suggested that the general performance of the algorithms for topographic correction ranks the three-factor+C correction, C correction, three-factor correction and Cosine correction from excellent to poor in order, which implies the promising potential of the proposed algorithms in effective topographic correction applications in remote sensing techniques.     

The Comparison of Reflectance based on Different Terrain Correction

Selecting the Xinglong Mountain which locates in the southeast of Lanzhou city，GanSu province as an example.Using the Landsat8 satellite image as the data source，the Cosine method，C and the modified Minnaert methods were used for each band in the study area.Comparing with the results of the field measurement reflectance and the statistical characteristics of image，the result showed that the cosine method has a perfect correction in the visible and short wave infrared wavelength，the C correction has a serious over\|correct，however.In the Near Infrared Wavelength，the better result obtained by C correction，and the cosine method has over\|correct otherwise.Comparing with the correct effect of whole bands，the modified Minnaert method has an ideal correct effect.The comparison of before and after correction we found that there is a smaller difference for three methods in the smaller slope，and the over correct is mostly in the south.What’s more，with the increase of the slope，the over correct is more obviously，but，there is a little over correct which used the modified Minnaert method in the whole area，it’s more suitable in large slope and the complicated area.     

卫星遥感影像数据的地形影响校正

地形影响校正是遥感影像辐射校正的主要内容，是获得地表真实反射率的必不可少的一步。本文提出的方法中，将6S大气校正模型与数字高程模型（DEM）结合起来，计算出水平地面上接收到的直接辐射与漫射辐射，并采用一个简单公式将其转化到地形坡面上，从而实现了对地面的辐射能量校正，同时，6S模型对卫片还进行了大气改正，可输出卫片在大气层底部的辐射亮度与反射率；然后将基于坡面的反射率换算到其在水平面上的对应值，即实现了对反射率的地形影响校正。在我们所实施的“滇金丝猴保护项目”的植被研究中，应用本方法对梅里雪山区域的ETM＋影像进行了校正，大大减小了地形对遥感数据的附加影响。     

The use of the Minnaert correction for land‐cover classification in mountainous terrain

Land‐cover classifications in mountainous terrain are often hampered by the topographic effect. Several strategies can be pursued to correct for this. A traditional approach is to use training areas for the same land‐cover class for different topographic positions and later merge those into one class. Other solutions involve topographic corrections, such as a Minnaert correction. In this study the classification result of the traditional training‐area approach was compared with the classification result of a Minnaert‐corrected image. In order to derive the Minnaert constants, a SPOT XS scene of the Santa Monica Mountains, USA, was divided into three visually relatively homogeneous regions. Eighty per cent of the pixels were assigned the same land cover in both classifications. Differences in classification were mainly in the section of the image that had more diverse land cover than in the more homogeneous chaparral‐covered eastern section. This supports previous findings that the Minnaert constant needs to be derived for individual land‐cover classes. The findings also suggest that after the Minnaert correction the resulting classification is comparable to the classification obtained using a more traditional approach.