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.     

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.     

Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis

This paper compares two general and formal solutions to the problem of fusion of multispectral images with high-resolution panchromatic observations. The former exploits the undecimated discrete wavelet transform, which is an octave bandpass representation achieved from a conventional discrete wavelet transform by omitting all decimators and upsampling the wavelet filter bank. The latter relies on the generalized Laplacian pyramid, which is another oversampled structure obtained by recursively subtracting from an image an expanded decimated lowpass version. Both the methods selectively perform spatial-frequencies spectrum substitution from an image to another. In both schemes, context dependency is exploited by thresholding the local correlation coefficient between the images to be merged, to avoid injection of spatial details that are not likely to occur in the target image. Unlike other multiscale fusion schemes, both the present decompositions are not critically subsampled, thus avoiding possible impairments in the fused images, due to missing cancellation of aliasing terms. Results are presented and discussed on SPOT data.     

Practical remote sensing image fusion method based on guided filter and improved SML in the NSST domain

Due to the different characteristics of image modality, the panchromatic (PAN) and multispectral (MS) images include complementary and redundancy information in the spatial and spectral resolutions. Image fusion is an effective way to integrate the source PAN and MS images to obtain high-resolution MS image. In this paper, a novel remote sensing image fusion scheme in non-subsample Shearlet transform (NSST) domain is presented. An enhancement strategy is designed to solve the insufficiency of spatial detail in multiresolution analysis (MRA)-based methods after the intensity–hue–saturation (IHS) color space transform. Then, in the NSST fusion process, a guided filter-based low-frequency coefficient fusion rule and an improved sum-modified-Laplacian (SML)-based high-frequency coefficient fusion rule are proposed. The final fused image can be obtained through the inverse NSST transform and inverse IHS transform. Two different groups of satellite dataset are utilized to evaluate the fusion performance. The experiment results demonstrate that the proposed approach can achieve more spatial details and less spectral distortion compared with the existing methods regarding both the visual quality and the objective measurements.     

基于非亚采样Contourlet和SWT的多光谱图像和全色图像的融合算法

该文研究了多尺度几何分析工具非亚采样Contourlet变换(NSCT),提出一种新的全色图像和多光谱图像融合的方法。该方法首先对全色图像和进行过IHS变换的多光谱图像的亮度分量进行NSCT变换,对于二者的低频近似系数再进行平稳小波变换(SWT)并融合,进一步提高融合图像的空间信息量,对于高频细节系数,采用基于局部平均梯度的方法进行融合,经过逆NSCT得到融合图像。实验结果表明,该文提出的方法在保留多光谱图像的光谱信息的同时,增强了融合图像的空间细节表现能力,提高了信息量,并且优于传统的基于IHS变换、小波变换、双树复小波变换及Contourlet变换的融合方法,该方法是有效可行的。     

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。     

基于Contourlet系数局部特征的选择性遥感图像融合算法

为了使融合后的多光谱图像在显著提高空间分辨率的同时,尽可能多地保持原始多光谱特性,提出了一种基于Contourlet变换系数局部特征的选择性遥感图像融合方法。根据多光谱和全色图像融合过程中Contourlet变换后的低频和高频部分融合目的的不同,对得到的近似和各层各方向的细节分量分别运用窗口邻域移动模板逐一计算相应区域Contourlet系数阵的不同局部特征量,然后选择适当的准则,对图像的近似和细节分量分别应用不同的策略在Contourlet系数域内进行选择性融合,通过Contourlet和亮度-色调-饱和度(IHS)逆变换得到融合的高分辨率多光谱图像。采用Landsat TM多光谱和SPOT全色图像进行的融合实验结果表明:提出的算法在显著提高空间分辨率的同时,又能很好地保持原始图像的光谱特征,并优于传统的融合方法。     

基于进化策略和IHS变换的图像融合方法

IHS变换法对多光谱图像和高分辨图像进行融合会丢失较多的光谱信息,本文利用进化策略来求解IHS变换法中光谱强度分量的最佳变换问题,经过变换后的光谱强度分量与多光谱图像的光谱强度分量和高分辨图像都具有较强的相关性,经过IHS反变换后可以得到同时具有较好的空间分辨率和光谱信息的融合图像.实验结果表明,该方法得到的融合图像优于传统的IHS变换法和小波变换方法.     

Estimation of the Number of Decomposition Levels for a Wavelet-Based Multiresolution Multisensor Image Fusion

The wavelet-based scheme for the fusion of multispectral (MS) and panchromatic (PAN) imagery has become quite popular due to its ability to preserve the spectral fidelity of the MS imagery while improving its spatial quality. This is important if the resultant imagery is used for automatic classification. Wavelet-based fusion results depend on the number of decomposition levels applied in the wavelet transform. Too few decomposition levels result in poor spatial quality fused images. On the other hand, too many levels reduce the spectral similarity between the original MS and the pan-sharpened images. If the shift-invariant wavelet transform is applied, each excessive decomposition level results in a large computational penalty. Thus, the choice of the number of decomposition levels is significant. In this paper, PAN and MS image pairs with different resolution ratios were fused using the shift-invariant wavelet transform, and the optimal decomposition levels were determined for each resolution ratio. In general, it can be said that the fusion of images with larger resolution ratios requires a higher number of decomposition levels. This paper provides the practitioner an understanding of the tradeoffs associated with the computational demand and the spatial and spectral quality of the wavelet-based fusion algorithm as a function of the number of decomposition levels     

改进的基于àtrous小波的遥感图像融合方法

针对多光谱与全色图像的融合,提出了一种改进的基于àtrous小波变换的图像融合方法。首先对多光谱图像进行IHS(intensity-hue-saturation)变换,然后对PAN图像进行àtrous小波变换,再将PAN图像的细节分量添加到多光谱图像的强度分量中。为了控制添加的数量,从小波系数中提取出一个重要性测度函数,并根据阈值选择重要的PAN特征,用新的强度分量来取代多光谱图像的强度分量。最后再做IHS逆变换得到融合图像。实验结果表明,和IHS变换算法及常用的小波融合算法相比,本文提出的融合方法在光谱信息的保持与空间细节信息的增强两个方面的综合性能得到提高,具有更好的效果。     

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。     

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。     

自适应字典学习的卷积稀疏表示遥感图像融合

基于细节注入方案的遥感图像融合主要包括两个步骤:空间细节提取与注入。为保证被提取细节的质量与确定合适的调制系数,本文提出一种基于自适应字典学习的卷积稀疏表示遥感图像融合方法。该方法先利用引导滤波和非抽取小波变换来分别获取全色图像和多光谱图像的空间细节;然后自适应地学习提取空间细节的字典,并将其引入卷积稀疏表示模型来重构联合细节图像;最后,将联合细节通过联合判别调制系数注入到上采样的多光谱图像中得到最终融合结果。实验结果表明,本文方法的融合结果无论从主观效果还是客观定量评价,都优于一些主流的遥感图像融合方法。      

Fusion of Multispectral and Panchromatic Images Using a Restoration-Based Method

Many remote-sensing satellites can obtain images in multispectral and panchromatic bands. By fusing low-resolution multispectral and high-resolution panchromatic images, one can obtain high-resolution multispectral images. In this paper, an image fusion algorithm based on image restoration is proposed to combine multispectral and panchromatic images. For remote-sensing satellites, the wavelength of the panchromatic band usually covers the wavelengths of the multispectral bands. This relationship between the two kinds of images is useful for fusion. In our approach, the low-resolution multispectral images are first resampled to the scale of the high-resolution panchromatic image. The relationship between these two kinds of images is then used to restore the resampled multispectral images. That is, the resampled multispectral images are modeled as the noisy blurred versions of the ideal multispectral images, and the high-resolution panchromatic image is modeled as a linear combination of the ideal multispectral images plus the observation noise. The ideal high-resolution multispectral images are then estimated based on the panchromatic and the resampled multispectral images. A closed-form solution of the fused images is derived here. Experiments show that the proposed fusion algorithm works effectively in integrating multispectral and panchromatic images.     

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.     

QuickBird Image Fusion by a Multirresolution-Multidirectional Joint Image Representation

A new fusion methodology for Multiespectral and Pancromatic images, has been proposed. This methodology is based on a joint multiresolution-multidirectional representation of the source images. For that an unique directional filters bank of low computacional complexity has been used.This kind of image representation allows an appropriated selection of the information extracted from the source images, avoiding some of the limitations inherent to other multiresolution fusión methods. The final aim is to obtain fused images with a high spectral and spatial quality simultaneously. The source images corresponds to the captured by Quickbird satellite (panchromatic and multispectral). The high quality of the obtained results shows the potential of the joint multiresolution-multidirectional representation for images fusion.     

一种基于非采样Contourlet变换的遥感图像融合算法

为使融合后的多光谱图像尽可能保持原多光谱图像光谱特性的同时提高空间质量,提出了一种基于非采样Contourlet变换（NSCT）和多尺度边缘检测的融合算法。介绍了非采样Contourlet变换和多尺度边缘检测;设计了基于多尺度边缘检测、直接替代的高频、低频子带融合规则;用QuickBird卫星高分辨率遥感图像进行仿真实验。实验结果表明该算法能够在保持光谱信息的同时注入更丰富的空间细节信息,优于传统的Wavelet变换法和Contourlet变换法。     

Generalized IHS‐Based Satellite Imagery Fusion Using Spectral Response Functions

Image fusion is a technical method to integrate the spatial details of the high‐resolution panchromatic (HRP) image and the spectral information of low‐resolution multispectral (LRM) images to produce high‐resolution multispectral images. The most important point in image fusion is enhancing the spatial details of the HRP image and simultaneously maintaining the spectral information of the LRM images. This implies that the physical characteristics of a satellite sensor should be considered in the fusion process. Also, to fuse massive satellite images, the fusion method should have low computation costs. In this paper, we propose a fast and efficient satellite image fusion method. The proposed method uses the spectral response functions of a satellite sensor; thus, it rationally reflects the physical characteristics of the satellite sensor to the fused image. As a result, the proposed method provides high‐quality fused images in terms of spectral and spatial evaluations. The experimental results of IKONOS images indicate that the proposed method outperforms the intensity‐hue‐saturation and wavelet‐based methods.     

Multiresolution reconstruction in fan-beam tomography

In this paper, a new multiresolution reconstruction approach for fan-beam tomography is established. The theoretical development assumes radial wavelets. An approximate reconstruction formula based on a near-radial quincunx multiresolution scheme is proposed. This multiresolution algorithm allows to compute both the quincunx approximation and detail coefficients of an image from its fan-beam projections. Simulations on mathematical phantoms show that wavelet decomposition is acceptable for small beam angles but deteriorates at high angles. The main applications of the method are denoising and wavelet-based image analysis.     

Image Fusion Processing for IKONOS 1-m Color Imagery

Many image fusion techniques have been developed. However, most existing fusion processes produce color distortion in 1-m fused IKONOS images due to nonsymmetrical spectral responses of IKONOS imagery. Here, we proposed a fusion process to minimize this spectral distortion in IKONOS 1-m color images. The 1-m fused image is produced from a 4-m multispectral (MS) and 1-m panchromatic (PAN) image, maintaining the relations of spectral responses between PAN and each band of the MS images. To obtain this relation, four spectral weighting parameters are added with the pixel value of each band of the original MS image. Then, each pixel value is updated using a steepest descent method to reflect the maximum spectral response on the fused image. Comparison among the proposed technique and existing processes [intensity hue saturation (IHS) image fusion, Brovey transform, principal component analysis, fast IHS image fusion] has been done. Our proposed technique has succeeded to generate 1-m fused images where spectral distortion has been reduced significantly, although some block distortions appeared at the edge of the fused images. To remove this block distortion, we also proposed a sharpening process using a wavelet transform, which removed block distortion without significant change in the color of the entire image.