共查询到19条相似文献，搜索用时 881 毫秒
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研究利用高光谱成像数据,用数字的方法定量模拟了ＳＯＰＴ－ＨＲＶ,ＣＢＥＲＳ－ＣＣＤ,Ｌａｎｄｓａｔ５－ＴＭ和ＮＯＡＡ１４－ＡＶＨＲＲ类似波段的空中表面反射率及地面光谱反射度图像,并利用这些对上述传感器相应波段的光谱响应,大气影响特性用基于反射率和基于归一化植被指数的方法进行了定量比较分析。 相似文献
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利用MIVIS数据进行遥感图像模拟的研究 总被引：12，自引：0，他引：12
本文研究利用机载ＭＩＶＩＳ超光谱成像系统数据,运用光谱模拟,大气改正的方法,定量模拟了ＳＰＯＴ－ＨＲＶ ＸＳ１、ＸＳ２、ＸＳ３的地面反射率图像,并将模拟结果与相应的地面反射率实测值进行了对比。同时对成像系统ＭＴＦ的影响也进行了定量模拟分析。试验结果显示,ＳＰＯＴ图像的模拟值与地面实测值相一致。 相似文献
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该文针对资源三号(ZY-3)国产高分辨率卫星影像的大气校正问题,提出了一种快速大气校正方法。该方法充分利用已有地面标准波谱库数据,并采用经验线性法对高分辨率ZY-3卫星遥感影像进行大气校正。最后利用地物的真实反射率光谱曲线和归一化植被指数(NDVI),与大气校正结果进行对比分析。结果表明:经新方法大气校正后的ZY-3影像地物反射率光谱曲线与地物真实反射率光谱曲线更加接近,植被覆盖区与非植被覆盖区NDVI的差异更加明显,更有利于地物的识别。研究结果为应用国产ZY-3影像进行定量遥感分析提供了参考。 相似文献
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以福建省平和县琯溪蜜柚为研究对象，利用星载Hyperion高光谱遥感数据对蜜柚叶片进行氮浓度估测。在分析Hyperion数据特征的基础上进行大气校正、几何纠正等预处理，从而得到图像反射率；结合地面光谱测量和蜜柚叶片采样分析，通过逐步回归分析法研究叶片氮浓度与高光谱图像反射率及其衍生量的关系，最终建立其遥感定量监测模型。结果表明，图像反射率的对数变换更有利于氮浓度的定量反演，入选的波段是983 nm、1 245 nm、1 316 nm和1 457 nm，其中1 245 nm波段对氮浓度影响最大，1 457 nm波段最小。利用该模型对氮浓度进行估算的值域与地面调查结果一致，说明利用高光谱进行氮浓度定量反演具有一定的可行性。 相似文献
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在高速公路环境遥感中,定量反演依赖精确的光谱反射率,因此,大气校正非常重要。本文基于高速公路路域植被环境遥感的大气校正特点,针对贵州省三凯高速路域的CBERS-02B卫星数据采用FLAASH大气校正,研究中结合路域环境遥感紧密相关的评价因子,分析校正前后路面、路域植被、河流和裸地的反射率和NDVI值变化,不同地物在其敏感波段的反射率更接近真值,校正后的NDVI也更接近利用地面实测数据的计算值。结果表明FLAASH大气校正能快速、较准确地消除大气因素对CBERS-02B数据的影响,能够有效地应用于路域影像纠正,服务于高速公路环境遥感监测。 相似文献
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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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HJ-1A CCD与TM数据及其估算草地LAI和鲜生物量效果比较分析 总被引：2，自引：1，他引：1
基于地面实测和PROSAIL模型模拟数据,研究了新型传感器HJ-1ACCD与TM数据一致性问题,分析了传感器天顶角和光谱相应函数差异的影响,对比两种传感器数据估算草地LAI和鲜生物量的效果,得出以下结论:(1)HJ-1ACCD和TM反射率数据的拟合系数R2在0.7322和0.9205左右,在反射率较小时,两种传感器数据一致性较好;随着反射率增大,HJ-1ACCD数值逐渐高于TM。总体而言,在可见光和近红外波段,两种传感器较为接近,其中红波段最接近。(2)两种传感器的NDVI数据一致性非常高,且受传感器天顶角和光谱响应函数影响作用较小(相对误差约为0.34%—0.53%),而反射率的相对差别在3.34%—9.54%。(3)传感器天顶角较光谱响应函数对反射率影响更大。(4)基于HJ-1ACCD反射率数据估算草地LAI和鲜生物量效果较好,其中以CCD2传感器估算效果最好。 相似文献
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CBERS-02 WFI的辐射交叉定标及其对植被指数的作用 总被引：1，自引：0，他引：1
本文以MODIS作为参考传感器，对CBERS-02星上的WFI传感器进行辐射交叉定标，得到WFI两个波段TOA辐亮度、表观反射率的增益与计数值偏移量，两个波段的计数值偏移量分别为42和21个DN左右。利用定标结果计算图像上另一均匀区的表观反射率。与MODIS反演的表观反射率比较验证。WFI与MODIS第1波段的表观反射率相差3．6％，第2波段相差-3．6％。检验结果说明，本文得到辐射交叉定标系数的精度能满足定量化应用的要求。为了说明辐射定标对植被指数的影响，本文选择5个实验区，比较分析了实验区图像辐射定标前后的NDVI。当WFI的DN值扣除定标得到的数字计数值偏移量后，DN值的NDVI与表观反射率的二次非线性拟合度可达99％。另外。WFI辐射定标前后的NDVI、RVI与MODIS反演的NDVI、RVI的比较分析说明，WFI辐射定标后表观反射率值的植被指数与MODI反演植被指数较接近，而且两个传感器RVI的差异小于NDVI的差异。 相似文献
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YE Zetian GU Xingfa LIU Xianlin WANG Zhimin 《地球空间信息科学学报》2000,3(2):35-40
1 ＩｎｔｒｏｄｕｃｔｉｏｎＩｎｍａｎｙｒｅｍｏｔｅｓｅｎｓｉｎｇａｐｐｌｉｃａｔｉｏｎｓ,ｔｈｅａｎａｌｙｓｉｓｏｆｔｈｅｍｕｌｔｉ＿ｓｅｎｓｏｒｓｄａｔａｆｒｏｍｄｉｆｆｅｒｅｎｔｔｉｍｅａｎｄｄｉｆｆｅｒｅｎｔｓｅｎｓｏｒｓｉｓｏｆｔｅｎｕｓｅｄｔｏｉｍｐｒｏｖｅｔｈｅｐｒｅｃｉ ｓｉｏｎｏｆｄａｔａｐｒｏｃｅｓｓｉｎｇ .Ｅｓｐｅｃｉａｌｌｙ ,ｉｎｔｈｅｄｙｎａｍｉｃｃｈａｎｇｅ＿ｍｏｎｉｔｏｒｉｎｇｏｆｅｎｖｉｒｏｎｍｅｎｔａｎｄａｇｒｉｃｕｌｔｕｒｅｒｅｍｏｔｅｓｅｎｓｉｎｇ ,ｐｅｏｐｌ… 相似文献
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This research, by use of RS image-simulating method, simulated apparent reflectance images at sensor level and ground-reflectance images of SPOT-HRV, CBERS-CCD Landsat-TM and NOAA14-AVHRR's corresponding bands. These images were used to analyze sensor's differences caused by spectral sensitivity and atmospheric impacts. The differences were analyzed on Normalized Difference Vegetation Index(NDVI). The results showed that the differences of sensors' spectral characteristics cause changes of their NDVI and reflectance. When multiple sensors' data are applied to digital analysis, the error should be taken into account. Atmospheric effect makes NDVI smaller, and atmospheric correction has the tendency of increasing NDVI values. The reflectance and their NDVIs of different sensors can be used to analyze the differences among sensor's features. The spectral analysis method based on RS simulated images can provide a new way to design the spectral characteristics of new sensors. 相似文献
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《International Journal of Digital Earth》2013,6(5):351-372
This study compares the spectral sensitivity of remotely sensed satellite images, used for the detection of archaeological remains. This comparison was based on the relative spectral response (RSR) Filters of each sensor. Spectral signatures profiles were obtained using the GER-1500 field spectroradiometer under clear sky conditions for eight different targets. These field spectral signature curves were simulated to ALOS, ASTER, IKONOS, Landsat 7-ETM+, Landsat 4-TM, Landsat 5-TM and SPOT 5. Red and near infrared (NIR) bandwidth reflectance were re-calculated to each one of these sensors using appropriate RSR Filters. Moreover, the normalised difference vegetation index (NDVI) and simple ratio (SR) vegetation profiles were analysed in order to evaluate their sensitivity to sensors spectral filters. The results have shown that IKONOS RSR filters can better distinguish buried archaeological remains as a result of difference in healthy and stress vegetation (approximately 1–8% difference in reflectance of the red and NIR band and nearly 0.07 to the NDVI profile). In comparison, all the other sensors showed similar results and sensitivities. This difference of IKONOS sensor might be a result of its spectral characteristics (bandwidths and RSR filters) since they are different from the rest of sensors compared in this study. 相似文献
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M. R. Pandya R. P. Singh K. N. Chaudhari K. R. Murali A. S. Kirankumar V. K. Dadhwal J. S. Parihar 《Journal of the Indian Society of Remote Sensing》2007,35(4):333-350
This paper reports the results of a modeling study carried out with two objectives, (1) to estimate and compare effective spectral characteristics (central wavelength, bandwidth and bandpass exo-atmospheric solar irradiance Eo) of various spectral channels of LISS-III, WiFS, LISS-III*, LISS-IV and AWiFS onboard Indian Remote Sensing Satellites IRS-ID and P6 using moment method based on the laboratory measurements of sensor spectral response, and (2) to quantify the influence of varying sensor spectral response on reflectance and Normalized Difference Vegetation Index (NDVI) measurements using surface reflectance spectra corresponding to different leaf area index conditions of crop target obtained through field experiment. Significant deviation of 4 to 14 nm in central wavelength and 1.6 to 14.07 nm in spectral width was observed for the corresponding channel of IRS sensors. Coefficient of variation of the order of 0.1 to 1.11% was noticed in Eo among various IRS sensors, which could induce a difference of 0.72 to 3.35% in the estimation of top of atmosphere reflectance for crop target. The variation in spectral response of IRS sensors implied a relative difference of the order of 0.91 to 3.38% in surface reflectance and NDVI measurements. Polynomial approximations are also provided for spectral correction that can be utilized for normalizing the artifacts introduced due to differences in spectral characteristics among IRS sensors. 相似文献
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Many sensors have their bands overlapped and therefore do not set a normal space. If a spectral distance is measured, as in first-order statistical classifiers, the direct consequence is that the result will not be the most accurate. Image classification processes are independent of the spectral response function of the sensor, so this overlap is usually ignored during image processing. This paper presents a methodology that introduces the spectral response function of sensors into the classification process to increase its accuracy. This process takes place in two steps: first, incident energy values of the sensors are reconstructed; second, the energy of the bands is set in an orthonormal space using a matrix singular value decomposition. Sensors with and without overlapping spectral bands were simulated to evaluate the reconstruction of energy values. The whole process was implemented on three types of images with medium, high and very high spatial resolution obtained with the sensors ASTER, IKONOS and DMC camera, respectively. These images were classified by ISODATA and minimum distance algorithms. The ISODATA classifier showed well-defined features in the processed images, while the results were less clear in the original images. At the same time, the minimum distance classifier showed that overall accuracy of the processed images increased as the maximum tolerance distance decreased compared to the original images. 相似文献
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本文应用3S技术生成数字高程模型(Digital Elevation Model,DEM),以山东半岛丘陵区为研究对象,解译出27条河流的流域范围。采用空间内插法计算流域内降水量,通过应用3S技术提取了相应年份的NDVI,最后对27个流域的降水量和NDVI进行了相关分析。结果表明:研究区内的NDVI较小,平均NDVI为0.0962;ND-VI与降水量在空间变化上存在一致性,关联程度极其显著(在0.01水平上),关联系数R=0.498>0.478=α0.01(26),呈线性回归关系。 相似文献
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The use of multispectral satellite sensors for generation of hyperspectral indices is restricted because of their coarse spectral resolutions. In this study, we attempted to synthesize a few of these hyperspectral indices, viz. RedEdge Normalized Difference Vegetation Index (NDVI705), Plant Senescence Reflectance Index (PSRI) and Normalized-Difference-Infrared-Index (NDII), for crop stress monitoring at regional scale using multispectral images, simulated from Hyperion data. The Hyperion data were resampled and simulated to corresponding spatial and spectral resolutions of AWiFS, OCM-2 and MODIS sensors using their respective filter function. Different possible combinations of two bands (i.e. simple difference, simple ratio and normalized difference) were computed using synthetic spectral bands of each sensor, and were regressed with NDVI705, PSRI and NDII. Models with highest correlation were selected and inverted on Hyperion data of another date to synthesize respective multispectral indices. Synthetic broad band indices of multispectral sensors with their respective narrow band indices of Hyperion were found to be in good agreement. 相似文献
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基于高光谱Hyperion数据的线性光谱混合模型与神经网络模型的比较 总被引：1，自引：1，他引：0
混合像元问题是定量遥感中的热点问题之一,为了改进从遥感数据中提取定量信息,人们建立了各种混合光谱分解技术,其中线性光谱混合模型和神经网络模型就是两种比较成熟的方法。以陕西省横山地区的高光谱Hyperion数据为研究基础,通过最小噪声变换(MNF)、像元纯度指数(PPI)转换和RMS误差分析的迭代方法相结合提取影像中的纯净像元作为终端端元。分别运用神经网络模型和线性光谱混合模型对影像进行光谱分解,得到各个组分的分解图像。以标准植被指数(NDVI)影像为衡量标准,选取训练样本点,分别对两种模型进行回归分析,结果显示NDVI影像与线性光谱混合模型植被分解图像的判定系数(R2=0.91)要大于其与神经网络模型的判定系数(R2=0.81)。进一步分析表明在一般情况下,线性光谱混合模型具有比神经网络模型略高的分离精度,但是神经网络模型对细部信息的提取的效果要好于线性光谱混合模型,最后提出了端元均方根误差(EAR)指数,一种新的混合像元分解的思路。 相似文献