共查询到16条相似文献，搜索用时 109 毫秒
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《测绘科学》2020,(4)
针对目前高空间和高时间分辨率覆盖全球观测的卫星亮度温度影像数据缺失问题,该文以中分辨率成像光谱仪(MODIS)和Landsat 8卫星数据为例,提出了两种不同融合策略生成亮度温度,并评估其融合效果:先融合-后反演和先反演-后融合,从而将MODIS亮温数据降尺度到Landsat 8空间分辨率。该实验过程采用时空自适应反射率融合模型(STARFM),并将预测生成的亮温数据与预测时刻Landsat8亮温数据进行对比分析,计算相关系数、结构相似性和均方根误差(RMSE)等指标。结果得出以STARFM为核心算法的两种融合策略均可较好地应用于生成高时间高空间分辨率的亮度温度,且先融合-后反演的策略相对于先反演-后融合的策略有更高的融合精度。 相似文献
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详细介绍了压缩感知的研究现状和发展趋势,对压缩感知理论的原理、稀疏基的选取、测量矩阵的构造和信号的重建进行了详细阐述。在图像超分辨率重建技术的基本模型的基础上,基于压缩感知的图像超分辨率重建模型,使用小波基作为稀疏基,并使用改进的正则化正交匹配追踪算法对单幅图像进行超分辨率重建。最后,进行仿真实验,实现了基于压缩感知的单幅图像超分辨率重建,并且和传统的超分辨率重建算法进行对比。结果表明,基于压缩感知的图像超分辨率重建算法,取得了比较好的效果。 相似文献
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融合Landsat ETM+和MODIS数据估算高时空分辨率地表短波反照率 总被引：2，自引：0，他引：2
提出一种通过融合高空间低时间分辨率、低空间高时间分辨率地表短波反照率,来估算高时空分辨率地表短波反照率的方法。首先,利用Landsat ETM+数据,通过窄波段到宽波段的转换得到一景或多景空间分辨率较高的ETM+蓝天空短波反照率;然后,在MODIS短波反照率产品基础上,以天空光比例因子为权重,得到空间分辨率较低的MODIS蓝天空短波反照率;最后,利用STARFM(Spatial and Temporal Adaptive Reflectance Fusion Model)模型融合ETM+短波反照率的空间变化信息和MODIS短波反照率的时间变化信息,得到高时空分辨率的地表短波反照率。针对STARFM模型在异质性区域估算精度降低的问题,通过以MODIS反照率影像各像元的端元(各地类)反照率取代MODIS像元反照率来提取时空变化等信息参与STARFM模型的融合过程,达到提高异质性区域估算精度的目的。结果显示,直接利用STARFM模型估算得到的高空间分辨率地表短波反照率处在合理的精度范围内(RMSE0.02),用改进后的STARFM模型估算得到的异质性区域短波反照率和真实ETM+短波反照率间的相关系数增大。 相似文献
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图像超分辨率重建技术是根据序列图像间信息互补重建高分辨率图像的技术,其主要步骤在于精确运动估计算法和有效超分辨率重建算法。针对存在旋转、缩放变换的序列图像,本文提出一种基于SIFT匹配和随机采样一致性算法（RANSAC）的运动估计算法,该方法首先使用SIFT算法对图像序列的特征点进行提取并匹配,然后使用RANSAC算法消除误匹配点并获取投影变换矩阵,从而获得图像序列间的亚像素级的运动信息;采用一组低分辨率序列图像进行试验,基于上述运动估计算法,采用迭代反投影进行超分辨率重建。试验结果表明,运动估计精度较高,重建影像具有较好的视觉效果,尤其适用于影像序列间存在旋转缩放运动的图像序列的超分辨率重建。 相似文献
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图像超分辨率重建是通过对单张或多张具有互补信息的低分辨率图像进行处理,重建一张高分辨率图像的技术。在单张图像的超分辨率重建中,基于稀疏表示的方法取得了很好的效果,得到了广泛的应用。一张图像中不同区域的图像块的内容一般会有显著变化。而基于稀疏表示的超分辨率重建算法多采用固定的字典,无法适应每一个图像块的重建需求。提出了一种结合外部数据和输入图像自身信息进行超分辨率重建的方法,通过搜索待处理图像块的非局部自相似块,结合在线字典学习方法对字典进行更新,从而保证更新后的字典能够匹配待处理的图像块。采用包括遥感图像在内的5张图像进行实验,并与4种经典的超分辨率重建算法进行比较,实验结果表明,此算法在主观评价和客观评价方面都有更好的表现。 相似文献
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《国土资源遥感》2021,(3)
时空数据融合能够有效提高高空间分辨率遥感数据的时间分辨率,但是目前广泛使用的时空自适应反射率融合模型在突变区域的预测效果不佳。针对这一问题,提出一种基于分层策略的时空融合模型(hierarchical spatial-temporal fusion model,H-STFM)。该模型首先根据相邻时刻低空间分辨率数据的反射率差值,将待预测的目标像元分为物候变化像元和突变像元;然后对物候变化像元进行线性回归预测,对突变像元进行加权滤波预测;最后将物候变化和突变区域的预测结果利用优化的时间加权函数融合生成最后预测图像。以两组中分辨率遥感数据MODIS和Landsat图像为基础数据进行实验对H-STFM模型进行了定性与定量评价。结果表明,提出模型的实验结果在方差误差与相对无量纲全局误差方面表现明显优于时空自适应融合模型。 相似文献
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针对MODIS图像分辨率受传感器限制和噪声干扰,且分辨率局限在一定水平等问题,提出一种采用主题学习和稀疏表示的MODIS图像超分辨率重建方法,该方法通过双边滤波将MODIS图像的平滑及纹理部分分离,并将纹理部分看成是由若干"文档"组成的训练样本;运用概率潜在语义分析提取"文档"的潜在语义特征,从而确定"文档"所属的"主题"。在此基础上,针对每个主题所对应的图像块,采用改进的K-SVD方法训练若干适用于不同主题的高低分辨率字典对,从而可以运用这些字典对,通过稀疏编码实现测试图像相应主题块的超分辨率重建。实验结果表明,重建图像在视觉效果和PSNR等指标上均优于传统方法。 相似文献
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结合像元分解和STARFM模型的遥感数据融合 总被引：4，自引：2，他引：2
高空间、时间分辨率遥感数据在监测地表快速变化方面具有重要的作用。然而,对于特定传感器获取的遥感影像在空间分辨率和时间分辨率上存在不可调和的矛盾,遥感数据时空融合技术是解决这一矛盾的有效方法。本文利用像元分解降尺方法(Downscaling mixed pixel)和STARFM模型(Spatial and Temporal Adaptive Reflectance Fusion Model)相结合的CDSTARFM算法(Combination of Downscaling Mixed Pixel Algorithm and Spatial and Temporal Adaptive Reflectance Fusion Model)进行遥感数据融合。首先,利用像元分解降尺度方法对参与融合的MODIS数据进行分解降尺度处理;其次,利用分解降尺度的MODIS数据替代STARFM模型中直接重采样的MODIS数据进行数据融合;最后以Landsat 8和MODIS遥感影像数据对该方法进行了实验。结果表明:(1)CDSTARFM算法比STARFM和像元分解降尺度算法具有更高的融合精度;(2)CDSTARFM能够在较小的窗口下获得更高的融合精度,在相同的窗口下其融合精度也高于STARFM;(3)CDSTARFM融合的影像更接近真实影像,消除了像元分解降尺度影像中的"图斑"和STARFM模型融合影像中的"MODIS像元边界"。 相似文献
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一种高时空分辨率NDVI数据集构建方法-STAVFM 总被引：1，自引：1，他引：0
ETM NDVI可以用来在30m的尺度上开展植被的监测,然而在Landsat卫星16天的重访周期和云污染等因素的影响下,常常会在相当长的一段时间内无法获取有效的ETM NDVI数据,给这一尺度下的植被动态监测带来了一定困难。相比之下,MODIS虽然在空间上只有250m分辨率的NDVI产品,却可以每天进行相同区域的监测。针对ETM空间分辨率高和MODIS时间分辨率高的特点,本研究选择实验区,基于对STARFM方法的改进,构建不同时空分辨率NDVI的时空融合模型-STAVFM,使用该模型对ETM NDVI与MODIS NDVI融合,构建了高时空分辨率NDVI数据集。研究结果表明,通过MODIS NDVI时间变化信息与ETM NDVI空间差异信息的有机结合,实现缺失高空间分辨率NDVI的有效预测（3景预测NDVI与实际NDVI的相关系数分别达到了0.82、0.90和0.91）,从而构建高时空分辨率NDVI数据集。所构建的高时空分辨率NDVI数据集在时间上保留了高时间分辨率数据的时间变化趋势,空间上又反映了高空间分辨率数据的空间细节差异。 相似文献
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Landsat 5卫星较低的时间分辨率(16天)使得其很难获得大区域的、时相一致的清晰影像数据集。本文发展了一种基于半物理模型的时空融合算法-即乘性调制融合算法,并借助多时序的MODIS反射率数据来生成多时相的Landsat TM/ETM+反射率合成影像,经镶嵌后得到区域尺度的高时空分辨率地表反射率数据集(Landsat TM/ETM+)。本文利用吉林省2006年—2011年的Landsat 5 TM地表反射率数据以及500 m的MOD09A1反射率产品来生成3个时相的Landsat 5 TM反射率合成数据,从而获得研究区在上述时相下地表反射率数据的镶嵌图。初步分析表明,所生成的Landsat 5 TM反射率数据的光谱分布特征与MOD09A1反射率数据较为一致,且图像在整体上光谱特征的连续性较好。 相似文献
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Mapping crop types is of great importance for assessing agricultural production, land-use patterns, and the environmental effects of agriculture. Indeed, both radiometric and spatial resolution of Landsat’s sensors images are optimized for cropland monitoring. However, accurate mapping of crop types requires frequent cloud-free images during the growing season, which are often not available, and this raises the question of whether Landsat data can be combined with data from other satellites. Here, our goal is to evaluate to what degree fusing Landsat with MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) data can improve crop-type classification. Choosing either one or two images from all cloud-free Landsat observations available for the Arlington Agricultural Research Station area in Wisconsin from 2010 to 2014, we generated 87 combinations of images, and used each combination as input into the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to predict Landsat-like images at the nominal dates of each 8-day MODIS NBAR product. Both the original Landsat and STARFM-predicted images were then classified with a support vector machine (SVM), and we compared the classification errors of three scenarios: 1) classifying the one or two original Landsat images of each combination only, 2) classifying the one or two original Landsat images plus all STARFM-predicted images, and 3) classifying the one or two original Landsat images together with STARFM-predicted images for key dates. Our results indicated that using two Landsat images as the input of STARFM did not significantly improve the STARFM predictions compared to using only one, and predictions using Landsat images between July and August as input were most accurate. Including all STARFM-predicted images together with the Landsat images significantly increased average classification error by 4% points (from 21% to 25%) compared to using only Landsat images. However, incorporating only STARFM-predicted images for key dates decreased average classification error by 2% points (from 21% to 19%) compared to using only Landsat images. In particular, if only a single Landsat image was available, adding STARFM predictions for key dates significantly decreased the average classification error by 4 percentage points from 30% to 26% (p < 0.05). We conclude that adding STARFM-predicted images can be effective for improving crop-type classification when only limited Landsat observations are available, but carefully selecting images from a full set of STARFM predictions is crucial. We developed an approach to identify the optimal subsets of all STARFM predictions, which gives an alternative method of feature selection for future research. 相似文献
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A new method was developed in this study for producing a clear-sky Landsat composite for cropland from cloud-contaminated Landsat images acquired in a short time period. It used Thiel–Sen regression to normalize all Landsat scenes to a MODIS image to make all Landsat images radiometrically consistent and comparable. Pixel selection criteria combining the modified maximum vegetation index and the modified minimum visible reflectance selection methods were designed to enhance the pixel selection of land/water over cloud/shadow in the image compositing. The advantages of the method include (1) avoiding complicated atmospheric corrections but with reliable surface reflectance results, (2) being insensitive to errors induced by image co-registration uncertainties between Landsat and MODIS images, (3) avoiding the lack of samples for the regression analysis using the full Landsat scenes (rather than overlay regions), and (4) enhancing cloud/shadow detection. The composite image has MODIS-like surface reflectance, thus making MODIS algorithms applicable for retrieving biophysical parameters. The method was automatically implemented on a set of 13 cloud-contaminated (>39%) Landsat-7 (Scan-Line Corrector-Off) and Landsat-8 scenes acquired during peak growing season in a crop region of Manitoba, Canada. The result was a 95.8% cloud-free image. The method can also substantially increase the usage of cloud-contaminated Landsat data. 相似文献
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高空间、高时间分辨率的遥感影像对地表与大气环境的实时精细监测具有重要作用,但单一卫星传感器获取的遥感影像存在空间与时间分辨率相互制约的问题,时空融合技术发展成为了低成本、高效生成满足不同应用需求的高时空分辨率遥感影像的有效手段.近年来,国内外学者提出了大量的时空融合算法,但对于复杂的地物类型变化的空间细节修复仍存在挑战... 相似文献
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Biodiversity mapping in extensive tropical forest areas poses a major challenge for the interpretation of Landsat images, because floristically clearly distinct forest types may show little difference in reflectance. In such cases, the effects of the bidirectional reflection distribution function (BRDF) can be sufficiently strong to cause erroneous image interpretation and classification. Since the opening of the Landsat archive in 2008, several BRDF normalization methods for Landsat have been developed. The simplest of these consist of an empirical view angle normalization, whereas more complex approaches apply the semi-empirical Ross–Li BRDF model and the MODIS MCD43-series of products to normalize directional Landsat reflectance to standard view and solar angles. Here we quantify the effect of surface anisotropy on Landsat TM/ETM+ images over old-growth Amazonian forests, and evaluate five angular normalization approaches. Even for the narrow swath of the Landsat sensors, we observed directional effects in all spectral bands. Those normalization methods that are based on removing the surface reflectance gradient as observed in each image were adequate to normalize TM/ETM+ imagery to nadir viewing, but were less suitable for multitemporal analysis when the solar vector varied strongly among images. Approaches based on the MODIS BRDF model parameters successfully reduced directional effects in the visible bands, but removed only half of the systematic errors in the infrared bands. The best results were obtained when the semi-empirical BRDF model was calibrated using pairs of Landsat observation. This method produces a single set of BRDF parameters, which can then be used to operationally normalize Landsat TM/ETM+ imagery over Amazonian forests to nadir viewing and a standard solar configuration. 相似文献