共查询到19条相似文献，搜索用时 356 毫秒
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环境星CCD数据大气校正研究 总被引：1，自引：0，他引：1
利用6S模型和同步气象资料,对国产环境与灾害监测预报小卫星HJ-1 A的CCD1传感器数据进行了大气校正和反射率反演。同时对CCD1传感器1～4波段大气校正前后的反射率变化进行了对比研究,发现大气校正后的1～3波段的地面反射率明显降低,4波段的地面反射率升高;利用同步野外实测地面数据对大气校正后的反射率数据进行了检验,两者结果基本一致;此外,还进行了定量化误差分析,以同步野外实测地面数据作为标准,将大气校正后的反射率数据与之对比,分析了可能带来误差的原因。结果表明,利用6S大气校正方法能够有效去除HJ-1 A星CCD图像的大气影响,获取地物绝对反射率。 相似文献
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采用飞马D200四旋翼无人机携带多光谱相机,获取了可见光波段的数据,地面同步测量了地表反射率和大气参数.结合实验数据,采用基于POS数据的严格成像模型和多项式模型对图像进行了几何校正,对比了2种模型的校正精度和不同重采样方式产生的差异.在几何校正的基础上进行了大气校正,获得地表反射率,选取典型地物水体、植被、裸土的实测结果进行验证.结果表明低空无人机遥感可以得到精度较高的地表反射率结果,但不同的几何校正模型对地表反射率影响较小,可以忽略.通过开展该实验证明了利用无人机开展定量化遥感研究的可行性. 相似文献
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黑河流域叶面积指数的遥感估算 总被引：7，自引：2，他引：7
研究利用Landsat7ETM+遥感数据获取黑河流域植被叶面积指数(LAI)空间分布的可行性。该研究是基于黑河流域分布式水文模型的一个重要输入项———LAI空间分布数据的需要而产生的。文章在详尽的野外观测数据基础上,分别探究实测LAI与同时相ETM+3、4、5、7波段反射率及相关植被指数(SR、NDVI、ARVI、RSR、SAV I、PVI、GESAVI)的相关关系,率定最佳的LAI遥感反演及其空间分布方案。研究发现,针对特定的自然条件,将研究区分为植被覆盖度小的稀疏立地和覆盖度大的密集立地,分别采用土壤调节植被指数(SAVI)和大气阻抗植被指数(ARVI)进行2种林地的LAI估算最为可靠,在此基础上,提出黑河地区LAI估算及其空间分布的遥感制图方案。 相似文献
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基于卫星数据的热红外图像模拟可以为热红外大气校正、地表温度和发射率反演和前期验证提供数据支撑,同时也可以为热红外传感器的波段设置和优化提供参考。热红外图像模拟是进行热红外定量遥感研究的有效手段。本研究利用Terra星上搭载的ASTER（Advanced Spaceborne Thermal Emission and Reflection Radiometer）遥感数据所反演得到的地表温度和发射率产品,基于高光谱辐射传输模型,模拟8—14 μm（714—1250 cm-1）,波谱分辨率为0.25 cm-1的星上TOA（Top of Atmosphere）高光谱热红外成像模拟数据。在此基础上,结合高光谱热红外图像数据的特点,实现了温度和发射率分离算法,以及对比了不同的基于图像的大气校正算法。结果显示,本文提出的热红外图像模拟方法可行,能够为评价不同的大气校正、温度和发射率分离算法提供有效的数据支撑。 相似文献
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地表反射率产品支持的GF-1 PMS气溶胶光学厚度反演及大气校正 总被引：1，自引：0，他引：1
GF-1卫星PMS(GF-1 PMS)数据具有高空间分辨率、短重访周期的特点,可以在地表类型识别、参数提取中发挥重要作用。但由于缺少2.1μm附近的短波红外波段,使得气溶胶反演时地表反射率的精确确定非常困难,从而导致其高精度大气校正难以开展,限制了该数据的应用。本文提出了一种地表反射率数据支持的气溶胶反演方法,用于GF-1PMS数据的大气校正。其基本思想是:使用现有的地表反射率数据集为GF-1PMS数据提供地表反射率,用于确定GF-1PMS图像中浓密植被像元(DDV)的分布,基于确定的浓密植被像元反演气溶胶光学厚度(AOD),并用于大气校正。这里使用的地表反射率数据集为合成的无云MODIS地表反射率产品,对GF-1PMS数据做了空间尺度的转换。为降低两类数据配准误差对地表反射率确定的影响,提出了使用区域NDVI分布百分比匹配的方法,回避了像元的直接匹配,为GF-1PMS数据提供DDV的空间分布。为验证该方法的有效性,利用北京、太湖两个AERONET站点观测的气溶胶光学厚度对气溶胶反演结果进行精度验证,结果表明,气溶胶反演算法精度较高,稳定性较强。AOD反演结果应用于北京和敦煌地区的GF-1PMS数据大气校正,获得的地表反射率与地面实测的地表反射率的误差低于0.015,且大气校正后影像对比度明显提高。 相似文献
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《测绘科学》2020,(4)
针对目前资源三号卫星数据缺少短波红外波段难以采用暗目标方法进行地表反射率反演的问题,该文提出一种基于最小反射率法获取大气参数结合6S辐射传射模型建立查找表进行大气校正的方法,并从校正前后影像值与地面实测反射率数据对比和归一化植被指数(NDVI)两个方面对校正效果进行了探讨。结果表明:①与5%、20%、40%、60%靶标的实测值对比,地表反射率值比表观反射率值更加接近靶标的真实测量值,反演的地表反射率与实测值绝对误差最大值为11.83%,最小值为0.21%,大气校正效果非常明显;②大气校正一方面增大了居民地与裸地的NDVI值,使水体与居民地、裸地易于区分,另一方面增大了植被与其他地物的NDVI差值,突出了植被信息,提高了提取植被的能力。 相似文献
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地形效应是影响遥感定量分析的主要障碍之一。尤其对于航空高光谱遥感而言,其地形效应更为显著,地形高程、角度带来的影响都不可忽略。基于青海雪鞍山地区的CASI高光谱影像和LiDAR地形数据,开展地形高程变化对航空高光谱遥感的影响研究。在假定每一个高程点为水平朗伯体的前提下,首先,基于MODTRAN软件模拟计算不同高程对应的大气上行辐射、地物至传感器之间的大气透过率、大气半球反照率和下行总辐射,进行地形高程变化对4个参量的影响分析;然后,设计实现了加入高程因子的大气辐射校正,完成了测区航空高光谱影像的反射率反演计算;最后,与FLAASH大气校正的反射率结果进行比较,发现同类地物的反射率曲线在谱形方面接近,但反射率数值存在差异,尤其是FLAASH大气校正结果中短波波段甚至出现负值,无疑是错误的。实验表明,高程因子的变化对山地航空高光谱影像成像过程的影响不可忽略,要实现精确的航空高光谱影像大气辐射校正必须消除其影响。 相似文献
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利用POLDER数据验证MODIS BRDF模型参数产品及Ross-Li模型 总被引：2，自引：2，他引：0
将MODIS BRDF模型参数产品(MCD43A1)模拟的近红外波段反射率与从POLDER-3/PARASOL BRDF全球数据集中筛选的9961个像元的BRDF观测数据进行对比,验证了MCD43A1所采用的RossThick-LiSparseR BRDF 模型(Ross-Li模型)拟合二向反射的能力。结果表明,Ross-Li模型总体上可以有效地模拟地物的二向反射,所有像元的近红外波段反射率模拟值与POLDER-3观测数据之间的R2达到0.943,RMSE为0.016,模拟反射率比POLDER-3数据总体偏低5.2%。但Ross-Li模型明显低估了热点反射率,热点模拟结果比POLDER-3数据平均偏低14%,模拟值的R2为0.824,RMSE为0.07。热点反射率模拟误差与地表覆盖类型有关,针叶林热点反射率模拟值偏低最严重,其次是阔叶林、草地与农田,灌木与裸地热点反射率模拟值偏低相对较小。通过修正Ross-Li模型中的体散射核,可以明显改善热点反射率的模拟效果(R2＝0.839,RMSE＝0.043)。Ross-Li模型对天底、暗点等特征方向反射率的模拟较为准确。Ross-Li模型的模拟精度随太阳天顶角和观测天顶角的增大而降低。对于农田与草地而言,Ross-Li模型的模拟精度随NDVI的增加而降低;但在森林与灌木覆盖条件下,当NDVI约为0.5时,Ross-Li模型的模拟效果最差。 相似文献
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Mapping of vegetation in mountain areas based on remote sensing is obstructed by atmospheric and topographic distortions. A variety of atmospheric and topographic correction methods has been proposed to minimize atmospheric and topographic effects and should in principle lead to a better land cover classification. Only a limited number of atmospheric and topographic combinations has been tested and the effect on class accuracy and on different illumination conditions is not yet researched extensively. The purpose of this study was to evaluate the effect of coupled correction methods on land cover classification accuracy. Therefore, all combinations of three atmospheric (no atmospheric correction, dark object subtraction and correction based on transmittance functions) and five topographic corrections (no topographic correction, band ratioing, cosine correction, pixel-based Minnaert and pixel-based C-correction) were applied on two acquisitions (2009 and 2010) of a Landsat image in the Romanian Carpathian mountains. The accuracies of the fifteen resulting land cover maps were evaluated statistically based on two validation sets: a random validation set and a validation subset containing pixels present in the difference area between the uncorrected classification and one of the fourteen corrected classifications. New insights into the differences in classification accuracy were obtained. First, results showed that all corrected images resulted in higher overall classification accuracies than the uncorrected images. The highest accuracy for the full validation set was achieved after combination of an atmospheric correction based on transmittance functions and a pixel-based Minnaert topographic correction. Secondly, class accuracies of especially the coniferous and mixed forest classes were enhanced after correction. There was only a minor improvement for the other land cover classes (broadleaved forest, bare soil, grass and water). This was explained by the position of different land cover types in the landscape. Finally, coupled correction methods showed most efficient on weakly illuminated slopes. After correction, accuracies in the low illumination zone (cos β ≤ 0.65) were improved more than in the moderate and high illumination zones. Considering all results, best overall classification results were achieved after combination of the transmittance function correction with pixel-based Minnaert or pixel-based C-topographic correction. Furthermore, results of this bi-temporal study indicated that the topographic component had a higher influence on classification accuracy than the atmospheric component and that it is worthwhile to invest in both atmospheric and topographic corrections in a multi-temporal study. 相似文献
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本文介绍了一种利用陆地卫星MSS图像进行水域信息机助识别提取的快速、经济而有效的方法。该方法以对水与非水信息的光谱特征,比值图像的数据结构特点和大气校正的研究与分析为基础。 研究表明,水与非水的主要差异集中于MSS4与MSS7波段,这两个波段的比值图像可在压抑阴影的同时将两图像的信息集中于一。根据对比值图像数据特点的分析,发现比值图像在作等比例拉伸并取整时所不可避免的信息损失主要集中在低值区,高值区则相对得到扩展增强。由于在MSS4/MSS7图像中,水的信息位于高值区,因此其水域识别能力优于MSS7/MSS4。但是,即使MSS4/MSS7图像也无法完全排除山区深阴影对水域识别的干扰。在进行MSS4/MSS7比值运算之前,首先对MSS4图像作粗略的大气校正,则可圆满地解决这一难题。在经大气校正后的MSS4/MSS7图像上,水体像元的值大于1,而深阴影及其它所有非水信息像元的值则小于1,因此,只要以此为门限值将图像二值化,即可获得精度很高的水域识别图像。完全消除阴影和其它因素的干扰。 本方法运算简单,处理速度为最小距离分类的3倍以上,且精度比分类方法高。它不仅适用于MSS图像,也适用于TM图像。采用本方法进行湖北省地表水域机助识别和面积测量的实际应用表明,在各种地貌类型区内本方法均能以较高的精度完成 相似文献
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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。 相似文献
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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 (NDVITHM) 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. 相似文献
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基于能量的地表反照率遥感反演方法研究 总被引：1，自引：0，他引：1
从能量传输方程入手，推导出地表入射辐射通量密度的简便计算公式； 根据反照率定义，提出基于能量的地表反照率反演新方法。用鄱阳湖地区TM遥感数据试验，其误差为4.91%。该方法不但简便易行，所需野外数据较少，物理意义明确，而且省去了窄波段反照率的计算过程，是一种遥感反演区域反照率的好方法。 相似文献
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以气象记录为辅助数据的TM影像大气校正方法 总被引：2，自引：3，他引：2
本文介绍一种LANDSATTM卫星影像校正方法,通过传感器校准、大气校正、地形校正等步骤获得反射率影像,卫星飞越时的大气参数是由当地气象记录的内插值导出,再以像幅内黑暗地物做调整,所用的大气传输模型为改进的Turner模型,可计算阳光直接辐照度、天光辐照度、路径亮度等参数,地形模型既可用来调整各参量随高程的变化,又可用以校正幅射的地形效应。 相似文献
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For surface features in short-wave infrared (SWIR, 1.3–3.0 μm) in remote sensing imagery, pixel values depict the total energy including reflection and emission. For surface features at normal temperature in SWIR band, emission energy can be ignored. While for surface features at high temperature in SWIR band, emission energy is equal to or even higher than the reflection energy. So remote sensing imagery of SWIR band can be used to separate emission and reflection energy as well as to realize temperature retrieval of high temperature targets. In this study, the seventh band (SWIR band) of Landsat8 OLI remote sensing imagery is used to perform the theoretical model research for temperature retrieval of high temperature targets. In the meantime, it is also used with the corresponding observation experiment of synchronization satellite to check the theoretical model. The result shows that the radiant flux density for mixed pixels with high temperature targets is higher than adjacent pixels without high temperature targets. Thus, the high temperature pixels can be identified in SWIR band. The retrieval results of temperature and fractional area for high temperature targets are consistent with reality. In the study, the result illustrates that it is effective to identify high temperature targets in remote sensing imagery of SWIR band and the model is appropriate for temperature retrieval use. 相似文献