基于边缘增强与光谱特性保持的Pan-sharpening融合模型

为生成兼具高光谱质量与高空间质量的融合图像,本文提出了一种新的Pan-sharpening变分融合模型.通过拟合退化后的全色（Panchromatic,Pan）波段图像与低分辨率多光谱（Multispectral,MS）波段图像间的线性关系得到各波段MS图像的权重系数,计算从Pan图像抽取的空间细节;基于全色波段图像的梯度定义加权函数,增强了图像的强梯度边缘并对因噪声而引入的虚假边缘进行了抑制,有效地保持了全色波段图像中目标的几何结构;基于MS波段传感器的调制传输函数定义低通滤波器,自适应地限制注入空间细节的数量,显著降低了融合MS图像的光谱失真;针对Pan-sharpening模型的不适定性问题,引入L1正则化能量项,保证了数值解的稳定性.采用Split Bregman数值方法求解能量泛函的最优解,提高了算法的计算效率.QuickBird、IKONOS和GeoEye-1数据集上的实验结果表明,模型的综合融合性能优于MTF-CON、AWLP、SparseFI、TVR和MTF-Variational等算法.     

调制传递函数对全色多光谱图像融合影响

针对高分辨率影像全色(Panchromatic,Pan)波段和多光谱(Multispectral,MS)波段的pan-sharpening融合后图像光谱失真的问题,基于调制传递函数(Modulation Transfer Function,MTF)的全色多光谱图像融合模型考虑到了多光谱图像的MTF值对融合图像质量的影响,采用了与多光谱图像相同的MTF值所构建的低通滤波器,得到较好的融合结果,但如何选择一个合适的MTF值还没有很好地解决。该文针对不同MTF值对模型融合结果的影响做了详细的分析与实验,并通过线性搜索的方式找出最优的MTF值。实验结果证明了该最优MTF能够同时提高模型融合结果的光谱细节和空间细节。     

基于深度金字塔网络的Pan-Sharpening算法

为了利用高空间分辨率单波段的全色(PAN)图像和低空间分辨率的多光谱图像(MS)生成高分辨率的多光谱图像,提出一种基于深度金字塔网络的遥感图像融合(即pan-sharpening)算法,通过图像金字塔的方式逐层上采样来重构高分辨率的多光谱图像.在细节保持方面,针对全色图像和多光谱图像在尺度上跨度过大的问题,采用深度金字塔网络多尺度地融合全色图像的细节信息;在光谱保持方面,使用反卷积层代替传统的超分辨算法来上采样低分率的多光谱图像;最后将这2部分相加,得到最终的融合图像. GeoEye-1数据集上的实验结果表明,文中算法综合性能优于BDSD, PRACS, PNN and PanNet算法.     

一种新的全色与多光谱图像融合变分模型

图像融合是提供包含各输入图像互补信息的单幅图像的有力工具. 本文提出了一种新的用于全色和多光谱图像融合的变分模型. 在Socolinsky对比度模型的基础上构造了一个改进的能量泛函最小化问题, 以寻找最接近全色图像梯度的解.为了提高多光谱图像的空间分辨率,并尽可能地保持其原有的光谱信息, 还将光谱一致项、波段间相关项和对比度增强项引入融合模型. 在IKONOS和QuickBird数据集上测试了该模型的性能.实验结果表明该模型可以生成同时具有高空间质量和高光谱质量的融合图像.     

结合空间-光谱调制及图像分割的多光谱图像融合方法

为了进一步提高多光谱(MS)图像与全色(PAN)图像之间的融合质量,平衡空间细节的注入与光谱信息的保持,提出了一种基于局部自适应空间-光谱调制与图像协同分割的融合方法.该方法利用k-means算法、根据MS图像的光谱特性进行图像分割,得到不同的连通体组,进而基于局部连通体组构建了局部自适应光谱调制(LASpeM)系数和局部自适应空间调制(LASpaM)系数,分别对融合图像中的光谱与空间信息进行调制;其中,LASpeM系数的构建基于MS和PAN图像中的细节提取以及MS波段之间的光谱关系, LASpaM系数的构建则基于MS和低分辨率PAN图像之间光谱特性的局部差异及相关性.另外,引入融合与分割的协同思想,利用图像分割来优化融合结果,并根据融合结果的反馈信息对分割算法的参数进行调整.在Matlab环境下,采用2个卫星GeoEye-1和QuickBird数据集进行融合实验,结果表明,文中方法在主观视觉与客观评价指标方面总体上优于7种经典及流行的融合方法,能够平衡融合图像的空间信息注入和光谱信息保持,有效地减少光谱扭曲.     

基于AIHS的遥感影像融合方法及其应用研究

针对传统的AIHS变换融合算法频谱失真问题,提出了一种改进的AIHS变换方法。该方法用多光谱影像边缘加权矩阵代替原有全色影像边缘加权矩阵,将全色图像加权矩阵与多光谱图像加权矩阵进行线性组合得到新的加权矩阵,进而决定全色图像注入到多光谱图像每个频带的空间细节量,从而得到IAIHS变换的定义式,即全色图像第i个频带与空间细节量的线性组合。实验结果表明,与传统AIHS变换方法相比,IAIHS变换方法可以很好地保持光谱质量。     

基于多种变换的遥感图像新型融合方法

针对多光谱图像空间分辨率低这一特点,提出一种在PCA变换基础上,利用小波变换和高通滤波相结合的图像融合算法。实现了ETM+全色波段与ETM+多光谱波段图像的融合,并从空间纹理信息,光谱真实性两个方面进行定性和定量评价。研究表明,该融合算法产生的光谱失真较小,同时很大程度地保持了高分辨率全色波段的空间纹理细节信息,是一种较好的图像融合方法。     

空间一致与波段相关的图像融合算法

针对目前遥感图像融合算法不能在提高空间信息的同时保真光谱信息的问题,提出基于空间和光谱约束的变分图像融合算法.首先基于各个波段融合前后的差异与观测到的空间差异一致假设,提出空间结构边缘自适应的约束项;然后基于融合前后各谱段之间相对关系不变假设,提出光谱波段比例一致性的约束项;最后将新的约束项引入到变分模型中,通过梯度下降法求解能量极小化问题得出融合结果.在Pleiades和Quick Bird数据集上进行实验,并与大量已有算法进行对比分析,结果表明,该算法可以生成高空间分辨率和光谱性保持优良的融合图像.     

通道融合的渐进增强遥感图像全色锐化算法

目的 遥感图像融合的目的是将低空间分辨率的多光谱图像和对应的高空间分辨率全色图像融合为高空间分辨率的多光谱图像。为了解决上采样多光谱图像带来的图像质量下降和空间细节不连续问题,本文提出了渐进式增强策略,同时为了更好地融合两种图像互补的信息,提出在通道维度上进行融合的策略。方法 构建了一种端到端的网络,网络分为两个阶段：渐进尺度细节增强阶段和通道融合阶段。考虑到上采样低空间分辨率多光谱图像导致的细节模糊问题,在第1阶段将不同尺度的全色图像作为额外的信息,通过两个细节增强模块逐步增强多光谱图像;在第2阶段,全色图像在多光谱图像的每个通道上都通过结构保持模块进行融合,更好地利用两种图像的互补信息,获得高空间分辨率的多光谱图像。结果 实验在GaoFen-2和QuickBird数据集上与表现优异的8种方法进行了比较,本文算法在有参考指标峰值信噪比(peak signal-to-noise ratio, PSNR)、结构相似度(structural similarity, SSIM)、相关系数(correlation coefficient, CC)和总体相对误差(erreur relative ...     

多光谱遥感图像与高分辨率全色图像融合研究

介绍了遥感图像融合的一般过程和特点,研究了像素级融合的常用算法,归纳了融合图像的基本步骤,采用四种融合方法对高空间分辨率的全色图像与高光谱分辨率的多光谱图像进行像素级融合实验,发现基于小波变换的图像融合提供更多细节信息,Brovey变换法融合全色图像与多光谱图像目视效果最好,速度最快。     

自适应权重注入机制遥感图像融合

目的 遥感图像融合是将一幅高空间分辨率的全色图像和对应场景的低空间分辨率的多光谱图像,融合成一幅在光谱和空间两方面都具有高分辨率的多光谱图像。为了使融合结果在保持较高空间分辨率的同时减轻光谱失真现象,提出了自适应的权重注入机制,并针对上采样图像降质使先验信息变得不精确的问题,提出了通道梯度约束和光谱关系校正约束。方法 使用变分法处理遥感图像融合问题。考虑传感器的物理特性,使用自适应的权重注入机制向多光谱图像各波段注入不同的空间信息,以处理多光谱图像波段间的差异,避免向多光谱图像中注入过多的空间信息导致光谱失真。考虑到上采样的图像是降质的,采用局部光谱一致性约束和通道梯度约束作为先验信息的约束,基于图像退化模型,使用光谱关系校正约束更精确地保持融合结果的波段间关系。结果 在Geoeye和Pleiades卫星数据上同6种表现优异的算法进行对比实验,本文提出的模型在2个卫星数据上除了相关系数CC（correlation coefficient）和光谱角映射SAM（spectral angle mapper）评价指标表现不够稳定,偶尔为次优值外,在相对全局误差ERGAS（erreur relative globale adimensionnelle de synthèse）、峰值信噪比PSNR（peak signal-to-noise ratio）、相对平均光谱误差RASE（relative average spectral error）、均方根误差RMSE（root mean squared error）、光谱信息散度SID（spectral information divergence）等评价指标上均为最优值。结论 本文模型与对比算法相比,在空间分辨率提升和光谱保持方面都取得了良好效果。     

Pan-sharpening using a guided filter

Pan-sharpening aims to integrate the spatial details of a high-resolution panchromatic (Pan) image with the spectral information of low-resolution multispectral (MS) images to produce high-resolution MS images. The key is to appropriately estimate the missing spatial details of the MS images while preserving their spectral contents. However, many existing methods extract the spatial details from the Pan image without fully considering the structures of the MS images, resulting in spectral distortion due to redundant detail injection. A guided filter can transfer the structures of the MS images into the intensity component or the low-pass approximation of the Pan image. Using the guided filter, we propose two novel pan-sharpening methods to reduce the redundant details among the MS and Pan images. Specifically, we extract the missing spatial details of the MS images by minimizing the difference between the Pan image and its corresponding filtering output, with the help of the MS images. Two different ways of using the MS images as guided images lead to two proposed methods, which can be grouped into component substitution (CS) family. Extensive experimental results over three data sets collected by different satellite sensors demonstrate the effectiveness of the proposed methods.     

Variational PCA fusion for Pan-sharpening very high resolution imagery

The Pan-sharpening approach based on principle component analysis(PCA) is affected by severe spectral distortion. To address this problem, a new pan-sharpening model based on PCA and variational technique is proposed to construct the substitute image of the first principal component(PC1). The energy functional consists of three terms. The first term injects PC1 with the geometric structure of the panchromatic(Pan) image. The second term preserves the spectral pattern of the multi-spectral image in the merged result.And the third term guarantees the smoothness of the functional optimization solution. The fusion result is given by the minimum of the energy functional, which is computed with the gradient descend flow. The experiments on QuickBird and IKONOS datasets validate the effectiveness of the proposed model. Compared with the stateof-the-art pan-sharpening approaches, this model exhibits a better trade-off between improving spatial quality and preserving spectral signature of the MS image.     

A regularised model-based pan-sharpening method for remote sensing images with local dissimilarities

Combining the spectral information of a low-resolution multispectral (LRMS) image and the spatial information of a high-resolution panchromatic (HRP) image to generate a high-resolution multispectral (HRMS) image has become an important and interesting issue. Local dissimilarities between the LRMS image and the HRP image affect the performance of the pan-sharpening technique. This paper presents a model-based pan-sharpening method with global and nonlocal spatial similarity regularisers to reduce the effects of the local dissimilarities. The degraded model relating the LRMS image to the unknown HRMS image is employed as the data-fitting term to keep spectral fidelity. Two spatial similarity constraints are utilized to further enhance the spatial resolution of the unknown HRMS image. The first regularisation term is under the assumption that the high-pass component of each HRMS band has the similar geometry structure with the adjusted high-pass component of the HRP image. A modulation matrix is constructed to reduce the contrast differences. Moreover, nonlocal self-similarity characteristic of the high-pass component extracted from each HRMS band is considered as another regulariser, which is an effective structural prior to improve the local spatial quality of the HRMS image. The weights of nonlocal similarity model are learned from the high-pass component of available HRP image. Experiments conducted on QuickBird and IKONOS data validate that the proposed pan-sharpening method can achieve better performance compared with several traditional and state-of-the-art pan-sharpening algorithms in terms of quantitative evaluation and visual analysis.     

Enhancing multispectral imagery spatial resolution using optimized adaptive PCA and high-pass modulation

ABSTRACT

The pan-sharpening scheme combines high-resolution panchromatic imagery (HRPI) data and low-resolution multispectral imagery (LRMI) data to get a single merged high-resolution multispectral image (HRMI). The pan-sharpened image has extensive information that will promote the efficiency of image analysis methods. Pan-sharpening technique is considered as a pixel-level fusion scheme utilized for enhancing LRMI using HRPI while keeping LRMI spectral information. In this article, an efficient optimized integrated adaptive principal component analysis (APCA) and high-pass modulation (HPM) pan-sharpening method is proposed to get excellent spatial resolution within fused image with minimal spectral distortion. The proposed method is adjusted with multi-objective optimizationto determine the optimal window size and σfor the Gaussian low-pass filter (GLPF) and gain factor utilized for adding the high-pass details extracted from the HRPI to the LRMI principlecomponent of maximum correlation. Optimization results show that if the spatial resolution ratio of HRPI to LRMI is 0.50, then a GLPF of 5 × 5 window size and σ = 1.640 yields HRMI with low spectral distortion and high spatial quality. If the HRPI/LRMI spatial resolution ratio is 0.25, then a GLPF of 7 × 7 window size and σ = 1.686 yields HRMI with low spectral distortion and high spatial quality. Simulation tests demonstrated that the proposed optimized APCA–HPM fusion scheme gives adjustment between spectral quality and spatial quality and has small computational and memory complexity.     

Remote-sensing image fusion using sparse representation with sub-dictionaries

Remote-sensing image fusion aims to obtain a multispectral (MS) image with a high spatial resolution, which integrates spatial information from the panchromatic (Pan) image and with spectral information from the MS image. Sparse representation (SR) has been recently used in remote-sensing image fusion method, and can obtain superior results to many traditional methods. However, the main obstacle is that the dictionary is generated from high resolution MS images (HRMS), which are difficult to acquire. In this article, a new SR-based remote-sensing image fusion method with sub-dictionaries is proposed. The image fusion problem is transformed into a restoration problem under the observation model with the sparsity constraint, so the fused HRMS image can then be reconstructed by a trained dictionary. The proposed dictionary for image fusion is composed of several sub-dictionaries, each of which is constructed from a source Pan image and its corresponding MS images. Therefore, the dictionary can be constructed without other HRMS images. The fusion results from QuickBird and IKONOS remote-sensing images demonstrate that the proposed method gives higher spatial resolution and less spectral distortion compared with other widely used and the state-of-the-art remote-sensing image fusion methods.     

Remote-sensing image fusion based on curvelets and ICA

Improving the quality of pan-sharpened multispectral (MS) bands is the main aim of the recent research on pan-sharpening. In this article, we present a novel image fusion method based on combining the curvelet transform and independent component analysis (ICA). The idea is to map the MS bands onto a statistically independent domain to determine the intensity component, which contains the common information of the MS bands, and then to pan-sharpen it using curvelets and a modified adaptive fusion rule. The proposed method is evaluated by visual and statistical analyses and compared with the curvelet (CVT)-based method using a context-based decision model, the CVT-based method using the Dempster–Shafer evidence theory, the improved ICA method, and the combined adaptive principle component analysis (PCA)–Contourlet method. The experimental results using QuickBird and WorldView-2 data show that the proposed method effectively reduces the spectral distortion while injecting spatial details into the fused bands as much as possible.