共查询到18条相似文献，搜索用时 160 毫秒
1.
本文从实际应用的角度出发,以城市地区的Landsat7ETM+影像和QuickBird影像为例,对小波法(WT)、Gram_Schimdt法(GS)、合成变量系数法(SVR)以及基于平滑滤波的亮度变换法(SFIM)四种融合方法在不同尺度的遥感影像之间的融合效果进行了研究,通过定性和定量评价指标对融合结果进行了评价。结果表明:在不同尺度遥感影像的融合中,SVR变换法具有最好的空间信息保留效果,GS变换法、SFIM法以及WT法分别次之;GS变换法具有最好的光谱信息保真效果,SVR法、WT法和SFIM法分别次之。 相似文献
2.
Landsat7 ETM＋影像的融合和自动分类研究 总被引：25，自引：0，他引：25
利用SFIM、MLT、HPF和修改的Brovey(MB)等遥感影像融合算法对Landsat 7 ETM 影像进行融合和自动分类研究，并就融合影像的光谱保真度、高频空间信息融人度和分类精度对这些方法进行评价。结果表明SFIM变换几乎完全保持了原始影像的光谱特点，并具有最高的平均分类精度；MB变换具有最高的高频空间信息融人度；MLT变换也具有较高的分类精度；只有HPF变换的各项指标都不突出。所有4种融合影像的分类精度都较原始影像的分类精度有明显的提高。这表明，源于同一传感器系统的不同分辨率影像的融合可以避免异源传感器融合影像所常见的各种参数、时相和配准误差，所以能够明显地提高影像的自动分类精度。 相似文献
3.
资源三号卫星影像融合算法对比分析 总被引：2，自引：0，他引：2
我国第一颗高精度民用立体测绘卫星——资源三号卫星搭载了2.1m分辨率的全色相机和5.8m分辨率的多光谱相机,针对资源三号影像研究有效的影像融合算法对于提高资源三号的影像质量和应用范围具有很大意义。文章分析了Pansharp、Gram-Schmidt、Modified IHS、改进的SFIM和Brovey 5种融合算法,梳理了影像融合的质量评价指标体系,采用资源三号卫星影像覆盖的农田、城区、林地、裸地等不同类型的区域开展几种融合算法试验,并对试验结果进行了分析评价。实验结果表明,5种融合算法中,Pansharp融合算法对资源三号影像数据融合效果最优,Gram-Schmidt融合算法次之,其次为Modified IHS影像融合算法,改进的SFIM融合算法和Brovey融合算法融合效果较差。 相似文献
4.
自适应高斯滤波与SFIM模型相结合的全色多光谱影像融合方法 总被引：1，自引：1，他引：0
全色-多光谱影像融合技术可以显著提高遥感影像的地物判别能力,但是空间信息融入度与光谱信息保真度是相互矛盾的一组性质,一般方法往往无法平衡这两方面。SFIM算法具有良好的光谱信息保持能力,但是其空间信息融入度较差,影响了整体的融合效果。为此,本文分析了SFIM模型的原理与特点,提出一种自适应高斯滤波与SFIM模型相结合的全色多光谱影像融合方法（AGSFIM）。以均值调整后的多光谱整体平均梯度为标准来计算高斯滤波的最优参数,将下采样全色影像的清晰度调整至同样水平,以保证融合结果的空间信息融入度与光谱信息保真度之间的平衡。利用6种融合算法对“北京二号”（Beijing-2）、“资源三号”02星（ZY-3 02）数据进行对比试验,表明在良好的光谱保持能力的前提下,改进方法可以有效克服SFIM算法空间信息融入不足的缺点。 相似文献
5.
《武汉大学学报(信息科学版)》2010,(10)
为了使融合影像能够最大限度地保持原始多光谱影像的光谱特征,同时又能尽可能地提高融合影像的空间分辨率,引入了模拟人眼视觉机理的高斯尺度空间技术,提出了一种基于高斯影像立方体的空间投影融合方法。该算法在避免光谱扭曲的同时,又能提高融合影像的空间分辨率。采用QuickBird遥感影像进行的实验结果表明,本方法在光谱偏差指数、相关系数、UIQI、ERGAS、运行时间、主观视觉感受等指标上较传统方法有显著优势。 相似文献
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7.
对ETM+影像进行多种算法融合实验,除应用ERDAS软件现有的融合方法(PCA、MLT)外,还利用IDL语言编程实现了SFIM、HPF、MB等融合算法,通过多次修改SFIM、HPF、MB融合算法中滤波器窗口的大小、滤波算子的实验,达到既不产生噪声又增强了图像纹理信息的融合效果。对融合后的影像进行了相同地物样本、不同分类方法的监督分类。以2002年内蒙古土地利用遥感调查数据为评价标准(内蒙古自治区遥感与地理信息系统重点实验室提供),用总体精度(overall accuracy)、kappa系数两种评价指数综合反映各种融合算法与各种分类方法结合的分类精度,并对各种分类方法及融合算法予以评价。 相似文献
8.
《国土资源遥感》2017,(1)
鉴于多源遥感影像融合受现有分辨率的限制,结合稀疏表示理论,提出了一种基于字典学习的遥感影像超分辨率融合方法,可将多光谱影像的空间分辨率提升到全色影像空间分辨率的1倍或2倍。在遥感影像融合框架下,首先建立学习字典,利用冗余字典对影像稀疏表示,重构超分辨率;然后采用Gram-Schmidt(GS)光谱锐化法,融合得到超分辨率多光谱影像。利用QuickBird数据对提出的方法进行3个实验,结果都表明本文方法相对传统融合方法、传统超分辨率方法和其他字典学习方案具有一定优势,适用于遥感影像超分辨率融合,可为多源遥感影像融合的超分辨率问题提供1种可行的解决方案,而且对其他融合方法也有借鉴意义。 相似文献
9.
融合多特征的遥感影像变化检测方法 总被引：1，自引：1，他引：0
本文提出了一种面向对象的多特征融合的变化检测方法。首先通过影像分割获取像斑,然后统计各像斑的光谱直方图和LBP(local binary patterns)纹理直方图,利用G统计量计算不同时期像斑之间的光谱距离和纹理距离,采用自适应的方法将光谱距离和纹理距离加权构建像斑的异质性,最后结合EM(expectation maximization)算法和贝叶斯最小错误率理论获取像斑的变化类别。在QuickBird影像上的实验表明该方法能够充分融合光谱特征和纹理特征,从而提高变化检测的精度。 相似文献
10.
在遥感图像处理中,由于应用的需要,已经有很多算法可以用来融合高分辨率的全色影像和低分辨率的多光谱影像。本文阐述了小波变换的Mallat算法和a′Trous（多孔）算法,在此基础上,作者提出了一种基于多分辨率分析的灰度调制影像融合方法（MRAGM算法）,以SPOT全色影像与TM多光谱影像融合为例,详细给出了算法和解算步骤,最后分别用三种方法对SPOT高分辨率全色影像和TM低分辨率多光谱影像进行了融合,并且对融合后的影像从定性和定量方面都进行了评价,可以得出MRAGM方法比Mallat算法和a′Trous（多孔）算法的融合效果要好。 相似文献
11.
Yangrong Ling Manfred Ehlers E. Lynn Usery Marguerite Madden 《ISPRS Journal of Photogrammetry and Remote Sensing》2007,61(6):381-392
Existing image fusion techniques such as the intensity–hue–saturation (IHS) transform and principal components analysis (PCA) methods may not be optimal for fusing the new generation commercial high-resolution satellite images such as Ikonos and QuickBird. One problem is color distortion in the fused image, which causes visual changes as well as spectral differences between the original and fused images. In this paper, a fast Fourier transform (FFT)-enhanced IHS method is developed for fusing new generation high-resolution satellite images. This method combines a standard IHS transform with FFT filtering of both the panchromatic image and the intensity component of the original multispectral image. Ikonos and QuickBird data are used to assess the FFT-enhanced IHS transform method. Experimental results indicate that the FFT-enhanced IHS transform method may improve upon the standard IHS transform and the PCA methods in preserving spectral and spatial information. 相似文献
12.
《地理信息系统科学与遥感》2013,50(5):687-710
This study examines best image fusion approaches for generating pansharpened very high resolution (VHR) multispectral images to be utilized for monitoring coastal barrier island development. Selected fusion techniques assessed in this research come from the three categories of spectral substitution (e.g., Brovey transform and multiplicative merging), arithmetic merging (e.g., modified intensity-hue-saturation and principal component analysis), and spatial domain (e.g., high-pass filter, and subtractive resolution merge). The image fusion methods selected for this study were capable of producing pansharpened VHR images with more than three bands. Comparisons of fusion techniques were applied to images from three satellite sensors: United States commercial satellites IKONOS and QuickBird, and the Korean KOMPSAT II. Pansharpened VHR multispectral images were assessed by spectral and spatial quality measurements. Results satisfying both spectral and spatial quality revealed optimum pansharpened techniques necessary for regular coastal mapping of barrier islands. These techniques may also be used to assess the quality of recently available VHR imagery acquired by numerous international, government, and commercial VHR satellite programs. 相似文献
13.
ALOS全色与多光谱影像融合的土地覆盖分类 总被引：1，自引：1，他引：0
利用Brovey、HighPass Filter和Gram-Schmidt 3种融合方法,对ALOS卫星全色与多光谱影像进行融合,并对融合后影像进行土地覆盖分类研究,从定性分析和比较融合后影像的分类精度2个方面综合评价了3种融合方法的效果。结果表明,3种融合方法都提高了影像的空间分辨率,Gram-Schmidt和HPF融合后影像光谱保持性好,同时3种融合方法不同程度上提高了影像的总体精度和Kappa系数,Gram-Schmidt最高,Brovey次之,HPF最弱,但对于不同地物分类精度又不尽相同,从整体分类结果来看,Gram-Schmidt最优。 相似文献
14.
Kunal Kumar Rai Aparna Rai Kanishka Dhar J. Senthilnath S. N. Omkar Ramesh K.N 《Journal of the Indian Society of Remote Sensing》2017,45(1):55-65
Image fusion techniques are widely used for remote sensing data. A special application is for using low resolution multi-spectral image with high resolution panchromatic image to obtain an image having both spectral and spatial information. Alignment of images to be fused is a step prior to image fusion. This is achieved by registering the images. This paper proposes the methods involving Fast Approximate Nearest Neighbor (FANN) for automatic registration of satellite image (reference image) prior to fusion of low spatial resolution multi-spectral QuickBird satellite image (sensed image) with high spatial resolution panchromatic QuickBird satellite image. In the registration steps, Scale Invariant Feature Transform (SIFT) is used to extract key points from both images. The keypoints are then matched using the automatic tuning algorithm, namely, FANN. This algorithm automatically selects the most appropriate indexing algorithm for the dataset. The indexed features are then matched using approximate nearest neighbor. Further, Random Sample Consensus (RanSAC) is used for further filtering to obtain only the inliers and co-register the images. The images are then fused using Intensity Hue Saturation (IHS) transform based technique to obtain a high spatial resolution multi-spectral image. The results show that the quality of fused images obtained using this algorithm is computationally efficient. 相似文献
15.
Nowadays, different image pansharpening methods are available, which combine the strengths of different satellite images that have different spectral and spatial resolutions. These different image fusion methods, however, add spectral and spatial distortions to the resultant images depending on the required context. Therefore, a careful selection of the fusion method is required. Simultaneously, it is also essential that the fusion technique should be efficient to cope with the large data. In this paper, we investigated how different pansharpening algorithms perform, when applied to very high-resolution WorldView-3 and QuickBird satellite images effectively and efficiently. We compared these 27 pansharpening techniques in terms of quantitative analysis, visual inspection and computational complexity, which has not previously been formally tested. In addition, 12 different image quality metrics available in literature are used for quantitative analysis purpose. 相似文献
16.
This study investigated the combined use of multispectral/hyperspectral imagery and LiDAR data for habitat mapping across parts of south Cumbria, North West England. The methodology adopted in this study integrated spectral information contained in pansharp QuickBird multispectral/AISA Eagle hyperspectral imagery and LiDAR-derived measures with object-based machine learning classifiers and ensemble analysis techniques. Using the LiDAR point cloud data, elevation models (such as the Digital Surface Model and Digital Terrain Model raster) and intensity features were extracted directly. The LiDAR-derived measures exploited in this study included Canopy Height Model, intensity and topographic information (i.e. mean, maximum and standard deviation). These three LiDAR measures were combined with spectral information contained in the pansharp QuickBird and Eagle MNF transformed imagery for image classification experiments. A fusion of pansharp QuickBird multispectral and Eagle MNF hyperspectral imagery with all LiDAR-derived measures generated the best classification accuracies, 89.8 and 92.6% respectively. These results were generated with the Support Vector Machine and Random Forest machine learning algorithms respectively. The ensemble analysis of all three learning machine classifiers for the pansharp QuickBird and Eagle MNF fused data outputs did not significantly increase the overall classification accuracy. Results of the study demonstrate the potential of combining either very high spatial resolution multispectral or hyperspectral imagery with LiDAR data for habitat mapping. 相似文献
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以Pleiades-1影像为对象,以探究其面向林地信息提取的融合算法选择为视角,通过基于目视和定量特征分析、辅助面向对象分类分析的方法,为林业部门遥感影像大规模融合应用提供参考。研究认为Pansharp和GramSchmidt算法融合结果目视效果良好,各波段与原多光谱相关系数均高达0.8以上,清晰度与纹理增强明显。两种融合算法影像在不同林地层次信息提取能力各有优势,Pansharp融合结果在林地层次分类总精度可达86.55%,Gram-Schmidt融合结果则在森林类型层次具有最高的分类总精度78.76%。具体融合算法的选取需根据其应用的信息提取层次而定。 相似文献