基于滤波器组的遥感图像融合方法及其性能分析研究

提出了一种基于滤波器组的图像融合方法,用以融合高空间分辨率全色图像和低空间分辨率的多光谱图像．在高空间分辨率全色图像经过多通道滤波器组分解的基础上,用多光谱图像直接替换全色图像低频子图像的方式进行融合处理;最后对替代后的子图像进行滤波器组重构得到融合后的图像．实验结果表明,通过调整滤波器组的通道个数,该方法能够使融合图像中空间信息和多光谱信息获得更好地折衷．     

基于NSCT的多光谱和全色图像的融合

提出了一种基于无下采样Contourlet变换的多光谱和全色图像的融合方法.该方法在对多光谱影像进行IHS变换的基础上,对多光谱的I分量和高分辨率的全色影像分别进行无下采样Contourlet变换(NSCT),然后对分解得到的近似分量以及各层金字塔各方向的细节分量利用本文提出的一定的融合准则分别对近似分量和细节分量进行影像融合,最后通过无下采样Conlourlet逆变换得到新的I分量,与H,S分量一起还原到RGB空间,最终得到融合后的高分辨率多光谱彩色图像.本文采用一组多光谱图像和全色图像数据进行融合实验,其实验融合图像的目视效果和统计指标均优于传统的IHS融合方法、小波融合方法以及Contourlet变换方法.     

A comparative analysis of image fusion methods

There are many image fusion methods that can be used to produce high-resolution multispectral images from a high-resolution panchromatic image and low-resolution multispectral images. Starting from the physical principle of image formation, this paper presents a comprehensive framework, the general image fusion (GIF) method, which makes it possible to categorize, compare, and evaluate the existing image fusion methods. Using the GIF method, it is shown that the pixel values of the high-resolution multispectral images are determined by the corresponding pixel values of the low-resolution panchromatic image, the approximation of the high-resolution panchromatic image at the low-resolution level. Many of the existing image fusion methods, including, but not limited to, intensity-hue-saturation, Brovey transform, principal component analysis, high-pass filtering, high-pass modulation, the a/spl grave/ trous algorithm-based wavelet transform, and multiresolution analysis-based intensity modulation (MRAIM), are evaluated and found to be particular cases of the GIF method. The performance of each image fusion method is theoretically analyzed based on how the corresponding low-resolution panchromatic image is computed and how the modulation coefficients are set. An experiment based on IKONOS images shows that there is consistency between the theoretical analysis and the experimental results and that the MRAIM method synthesizes the images closest to those the corresponding multisensors would observe at the high-resolution level.     

基于多级引导滤波器的图像区域融合算法

为了提高多光谱图像与全色图像的融合质量,研究了多种滤波器和融合算法,提出了基于多级引导滤波器的区域融合方法。采用该方法对多光谱图像进行插值,利用改进的分水岭算法对全色图像进行区域划分,并将划分结果映射至每个多光谱图像,然后将多光谱图像与全色图像利用多级引导滤波器分别进行滤波,得到各自的细节信息,最后根据每个区域中全色图像和多光谱图像的关系指标局部相关系数与4阶相关系数的大小,对细节信息进行区域融合,得到融合后的多光谱图像。结果表明,该算法充分保留了多光谱图像的光谱信息,并尽可能多地注入了全色图像的细节信息,成功地提高了多光谱图像的融合效果。     

Remote Sensing Image Fusion Using Multiscale Mapped LS-SVM

The panchromatic (Pan) sharpening of multispectral (MS) bands is an important technique in the various applications of satellite remote sensing. This paper presents an MS Pan- sharpening method using the proposed multiscale mapped least-squares support vector machine (LS-SVM). Under the LS-SVM framework, the salient features underlying the image are represented by support values, and the support value transform (SVT) is developed for image information extraction. The low-resolution MS bands are resampled to the fine scale of the Pan image and sharpened by injecting the detailed features extracted from the high-resolution Pan image. The support value analysis is implemented by using a series of multiscale support value filters that are deduced from the mapped LS-SVM with multiscale Gaussian radial basis function kernels. Experiments are carried out on very high resolution QuickBird MS + Pan data. Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed MS Pan-sharpening method performs comparable to the state-of-the-art in terms of the pertained quantitative quality evaluation indexes, such as the Spectral Angle Mapper, relative dimensionless global error in synthesis (ERGAS), modulation-transfer-function-based tool and quality index (Q4), etc. The SVT is an effective tool for remote sensing image fusion.     

基于变分的多尺度遥感图像融合算法

全色图像与多光谱图像融合时,忽略了上采样的重要性和通道间细节的差异性.针对前者,利用不同尺度下自相似块,估计出低分辨率图像丢失信息,从而修改了图像上采样的策略,并以此构造目标函数的保真项;针对后者,利用全色图像和光谱图像梯度域结构相似性,提出局部加权动态稀疏约束,构造目标函数的正则项.本文基于变分法理论,构造了新的目标函数,并提出了多尺度迭代融合框架,通过多次迭代逐步提高融合图像的分辨率,每一层的融合结果更加准确,从而提高最终的融合精度.本文算法与Brovey等成分替代算法、P+XS等变分算法、MTF_GLP等多分辨分析算法进行比较.实验结果表明,本算法的融合结果具有良好的视觉效果,且在客观评价指标上比所有对比算法的最优值平均值均有提高.     

一种图像融合新算法

将小波的多分辨分解、进化策略与IHS变换有机地结合起来,提出了一种多光谱与高分辨率图像融合的新算法。该方法首先依据高分辨率图像经小波分解得到的各小波面,进行区域划分,再采用分区域加边缘有效因子的融合思想,使得融合图像最大限度地保留了多光谱图像的光谱信息和高分辨率图像的空间分辨率。文中给出了该方法的融合结果,并与IHS法、MWD法进行了比较,证明了该图像融合方法的有效性。     

Super Resolution of Multispectral Images using ℓ1 Image Models and Interband Correlations

In this paper we propose a novel super-resolution based algorithm for the pansharpening of multispectral images. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images; that is, it imposes smoothness within each band by means of the energy associated with the ℓ1 norm of vertical and horizontal first order differences of image pixel values and also takes into account the correlation among the bands of the multispectral image. The observation process is modeled using the sensor characteristics of both panchromatic and multispectral images. The method is tested on real and synthetic images, compared with other pansharpening methods, and the quality of the results assessed both qualitatively and quantitatively.     

改进的PCNN模型在多光谱与全色图像融合中的应用研究

介绍了PCNN模型原理,提出了基于双通道自适应的PCNN多光谱与全色图像融合算法。该算法首先将RGB空间的多光谱图像转换为HSV彩色空间,然后将HSV彩色空间中的非彩色通道(V通道)的灰度像素值和全色图像的像素灰度值分别作为PCNN-1及PCNN-2的神经元输入,利用方向性信息作为自适应链接强度系数,对非彩色通道图像和全色图像进行自适应分解,再将点火时间序列送入判决因子得到新的非彩色通道图像,最后将原多光谱图像的H通道分量、S通道分量及新的V通道分量经HSV空间逆变换获得最终的融合图像。实验结果表明,该算法不仅解决了链接强度系数自动设置的问题,而且充分考虑到图像边缘和方向特征的影响,无论在主观视觉效果,还是客观评价标准上均优于IHS、PCA、小波融合等其他图像融合算法,同时降低了计算复杂度。     

独立成分分析的多光谱图像融合算法

为解决图像空间信息的问题,文章提出了一种独立成分分析的多光谱图像融合算法,将多光谱图像的RGB 3个波段和近红外图像共4个波段进行独立成分分析变化,并对其做加权平均得到主图像信息,将主图像信息与全色图像加权求平均得到一副新的图像,然后将这幅图像还原到4个波段得到融合后的结果图像。     

High spectral quality pansharpening approach based on MTF-matched filter banks

Pansharpening consists in merging a low-resolution multispectral image (MS) with a high spatial resolution panchromatic image (PAN) to produce a high resolution pansharpened MS image. It consists in enhancing spatially the low-resolution MS image by injecting the missing details provided by the high-resolution PAN image. In this paper, we propose a novel pansharpening approach based on decomposition/reconstruction processing using low-pass and high-pass filter banks. On the one hand, the low-pass approximation (taking into account the imaging system modulation transfer function MTF) of the pansharpened MS image is assumed to be equal to the original MS image in order to preserve the spectral quality. On the other hand, the high-pass filter allowing us to extract the high-frequency PAN details is designed as complementary filter to the low-pass one in order to provide perfect reconstruction in the ideal case. Quantitative assessment performed on reduced and full-resolution images are used to validate the proposed technique and compare it to state-of-art. Experimental results using Pléaides and GeoEye-1 data show that our proposed fusion schema outperforms the pre-existing methods visually as well as quantitatively.     

Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods

Usual image fusion methods inject features from a high spatial resolution panchromatic sensor into every low spatial resolution multispectral band trying to preserve spectral signatures and improve spatial resolution to that of the panchromatic sensor. The objective is to obtain the image that would be observed by a sensor with the same spectral response (i.e., spectral sensitivity and quantum efficiency) as the multispectral sensors and the spatial resolution of the panchromatic sensor. But in these methods, features from electromagnetic spectrum regions not covered by multispectral sensors are injected into them, and physical spectral responses of the sensors are not considered during this process. This produces some undesirable effects, such as resolution overinjection images and slightly modified spectral signatures in some features. The authors present a technique which takes into account the physical electromagnetic spectrum responses of sensors during the fusion process, which produces images closer to the image obtained by the ideal sensor than those obtained by usual wavelet-based image fusion methods. This technique is used to define a new wavelet-based image fusion method.     

联合多流融合和多尺度学习的卷积神经网络遥感图像融合方法

为尽可能保持原始低分辨率多光谱(LRMS)图像光谱信息的同时,显著提高融合后的多光谱图像的空间分辨率,该文提出一种联合多流融合和多尺度学习的卷积神经网络遥感图融合方法。首先将原始MS图像输入频谱特征提取子网得到其光谱特征,然后分别将通过梯度算子处理全色图像得到的梯度信息和通过卷积后的全色图像与得到的光谱特征图在通道上拼接输入到具有多流融合架构的金字塔模块进行图像重构。金字塔模块由多个骨干网络组成,可以在不同的空间感受野下进行特征提取,能够多尺度学习图像信息。最后,构建空间光谱预测子网融合金字塔模块输出的高级特征和网络前端的低级特征得到具有高空间分辨率的MS图像。结合WorldView-3卫星获取的图像进行实验,结果表明,所提方法生成的融合图像在主观目视检验和客观评价指标上都优于大多先进的遥感图像融合方法。     

QuickBird高分辨率影像融合方法比较研究

遥感图像融合的关键就是选择合适的融合方法,恰当的融合方法可在提高图像空间信息的同时,能很好地保持多光谱影像的光谱特性。从应用的角度出发,利用ENVI 4.5、PCI 9.0、ERDAS 9.1和PhotoShop四种软件平台对快鸟卫星(QuickBird)数据进行了融合实验研究,并对相应的融合结果进行了讨论与分析,希望为同类作业者提供借鉴。结果表明,格拉姆-施密特(Gram-schmidt)正交化变换法和彩色合成(PanSharp)方法在提高影像空间分辨率的同时,还较好地保持了多光谱波段的光谱信息,并且色彩信息更符合人的视觉习惯。     

A Model-Based Approach to Multiresolution Fusion in Remotely Sensed Images

In this paper, a model-based approach to multiresolution fusion of remotely sensed images is presented. Given a high spatial resolution panchromatic (Pan) image and a lowspatial resolution multispectral (MS) image acquired on the same geographical area, the presented method aims to enhance the spatial resolution of the MS image to the resolution of the Pan observation. The proposed fusion technique utilizes the spatial correlation of each of the high-resolution MS channels by using an autoregressive (AR) model, whose parameters are learnt from the analysis of the Pan data. Under the assumption that the parameters of the AR model for the Pan image are the same as those that represent the MS images due to spectral correlation, the proposed technique exploits the learnt parameter values in the context of a proper regularization technique to estimate the high spatial resolution fields for the MS bands. This results in a combination of the spectral characteristics of the low-resolution MS data with the high spatial resolution of the Pan image. The main advantages of the proposed technique are: 1) unlike standard methods proposed in the literature, it requires no registration between the Pan and the MS images; 2) it models effectively the texture of the scene during the fusion process; 3) it shows very small spectral distortion (as it is less affected, compared to standard methods, by the specific digital numbers of pixels in the Pan image, since it exploits the learnt parameters from the Pan image rather than the actual Pan digital numbers for fusion); and 4) it can be used in critical situations in which the Pan and the MS images are acquired (also by different sensors) in slightly different areas. Quantitative experimental results obtained using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Quickbird images point out the effectiveness of the proposed method.     

Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition

Since Chavez proposed the highpass filtering procedure to fuse multispectral and panchromatic images, several fusion methods have been developed based on the same principle: to extract from the panchromatic image spatial detail information to later inject it into the multispectral one. In this paper, we present new fusion alternatives based on the same concept, using the multiresolution wavelet decomposition to execute the detail extraction phase and the intensity-hue-saturation (IHS) and principal component analysis (PCA) procedures to inject the spatial detail of the panchromatic image into the multispectral one. The multiresolution wavelet decomposition has been performed using both decimated and undecimated algorithms and the resulting merged images compared both spectral and spatially. These fusion methods, as well as standard IHS-, PCA-, and wavelet-based methods have been used to merge Systeme Pour l'Observation de la Terre (SPOT) 4 XI and SPOT 4 M images with a ratio 4:1. We have estimated the validity of each fusion method by analyzing, visually and quantitatively, the quality of the resulting fused images. The methodological approaches proposed in this paper result in merged images with improved quality with respect to those obtained by standard IHS, PCA, and standard wavelet-based fusion methods. For both proposed fusion methods, better results are obtained when an undecimated algorithm is used to perform the multiresolution wavelet decomposition.     

基于离散向量小波变换的多光谱图像融合

为了能够更好地把多光谱图像和高分辨率图像的图像信息综合起来,以提高对图像信息的分析和提取能力,在研究了离散向量小波图像分析法之后,本文提出了一种基于离散向量小波变换的像素级图像融合新方法。向量小波作为标量小波的一种扩展具有很多标量小波所没有的优点。向量小波分析能够为图像提供一种比标量小波多分辨分析更加精细的分析方法。利用向量小波将多光谱图像和高分辨率图像进行融合后得到的融合图像,能够很好地将原图像的细节融合在一起。给出了该方法的融合结果,通过与其它融合方法进行主观与客观的评价比较,证明该融合方法可以获得更好的融合效果。     

基于多流融合生成对抗网络的遥感图像融合方法

由于强大的高质量图像生成能力,生成对抗网络在图像融合和图像超分辨率等计算机视觉的研究中得到了广泛关注。目前基于生成对抗网络的遥感图像融合方法只使用网络学习图像之间的映射,缺乏对遥感图像中特有的全锐化领域知识的应用。该文提出一种融入全色图空间结构信息的优化生成对抗网络遥感图像融合方法。通过梯度算子提取全色图空间结构信息,将提取的特征同时加入判别器和具有多流融合架构的生成器,设计相应的优化目标和融合规则,从而提高融合图像的质量。结合WorldView-3卫星获取的图像进行实验,结果表明,所提方法能够生成高质量的融合图像,在主观视觉和客观评价指标上都优于大多先进的遥感图像融合方法。

     

基于非负矩阵分解的TerraSAR与SPOT5影像融合

合成孔径雷达具有全天候、全天时的优势,能够提供高分辨的目标图像,而多光谱遥感影像能够提供丰富的光谱信息,将两种不同影像进行融合,综合两者的优势信息可以得到质量更高、信息更丰富的图像。提出了基于非负矩阵分解算法的融合算法对SAR影像和多光谱遥感影像进行融合。通过对比研究,提出的融合算法在目视判读以及客观评价指标上和传统融合算法相比,都有较明显的改善,尤其是在细节表现力和光谱保持度方面优势显著。     

一种超高分辨率遥感图像融合新算法

该文针对超高分辨率的全色光图像和多光谱图像的融合,提出了一种基于对应分析的图像融合新算法。该算法在对多光谱数据进行对应分析的基础上,利用冗余小波变换提取出全色光图像的空间细节信息并将其融入到成分空间。实验分别采用IKONOS和QuickBird数据,融合结果的目视效果与客观评价表明,相比现有同类融合方法,该方法能够在提高空间分辨率的同时更好地保持光谱特性,有效地减少了色彩失真的现象。