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。     

Fusion and registration of THEOS multispectral and panchromatic images

This article presents a new method for the fusion and registration of THEOS (Thailand Earth Observation Satellite) multispectral and panchromatic images in a single step. In the usual procedure, fusion is an independent process separated from the registration process. However, both image registration and fusion can be formulated as estimation problems. Hence, the registration parameters can be automatically tuned so that both fusion and registration can be optimized simultaneously. Here, we concentrate on the relationship between low-resolution multispectral and high-resolution panchromatic imagery. The proposed technique is based on a statistical framework. It employs the maximum a posteriori (MAP) criterion to jointly solve the fusion and registration problem. Here, the MAP criterion selects the most likely fine resolution multispectral and mapping parameter based on observed coarse resolution multispectral and fine resolution panchromatic images. The Metropolis algorithm was employed as the optimization algorithm to jointly determine the optimum fine resolution multispectral image and mapping parameters. In this work, a closed-form solution that can find the fused multispectral image with correcting registration is also derived. In our experiment, a THEOS multispectral image with high spectral resolution and a THEOS panchromatic image with high spatial resolution are combined to produce a multispectral image with high spectral and spatial resolution. The results of our experiment show that the quality of fused images derived directly from misaligned image pairs without registration error correction can be very poor (blurred and containing few sharp edges). However, with the ability to jointly fuse and register an image pair, the quality of the resulting fused images derived from our proposed algorithm is significantly improved, and, in the simulated cases, the fused images are very similar to the original high resolution multispectral images, regardless of the initial registration errors.     

An image fusion method based on object-oriented classification

In image fusion of different spatial resolution multispectral (MS) and panchromatic (PAN) images, a spectrally mixed MS pixel superimposes multiple mixed PAN pixels and multiple pure PAN pixels. This verifies that with increased spatial resolution in imaging, a low spatial resolution spectrally mixed subpixel may be unmixed to be a pure pixel. However, spectral unmixing of mixed MS subpixels is rarely considered in current remote-sensing image fusion methods, resulting in blurred fused images. In the image fusion method proposed in this article, such spectral unmixing is realized. In this method, the MS and PAN images are jointly segmented into image objects, image objects are classified to obtain a classification map of the PAN image and each MS subpixel is fused to be a pixel matching the class of the corresponding PAN pixel. Tested on spatially degraded IKONOS MS and PAN images with a significant spatial resolution ratio of 8:1, the fusion method offered fused images with high spectral quality and deblurred visualization.     

基于归一化相关矩的多分辨率遥感图象融合

多传感器数据融合技术已广泛应用于遥感图象处理方面 .针对遥感多光谱图象空间分辨率较低的问题 ,提出了一种基于归一化相关矩的多分辨率图象融合方法 .该方法首先对图象进行二维小波变换 ,然后根据所得到的高频小波系数的一阶、二阶统计特征来定义图象局部灰度相关矩 ,并以此作为图象融合测度来对遥感图象进行多分辨率特征融合 ,从而得到包含更多信息和有效特征的融合图象 .仿真结果表明 ,融合后的图象在保留多光谱信息和提高空间分辨率上均能获得较好的效果 ,因而可以更好地用于目标识别、分类等遥感图象处理方面     

Spatio-spectral fusion of satellite images based on dictionary-pair learning

This paper proposes a novel spatial and spectral fusion method for satellite multispectral and hyperspectral (or high-spectral) images based on dictionary-pair learning. By combining the spectral information from sensors with low spatial resolution but high spectral resolution (LSHS) and the spatial information from sensors with high spatial resolution but low spectral resolution (HSLS), this method aims to generate fused data with both high spatial and spectral resolution. Based on the sparse non-negative matrix factorization technique, this method first extracts spectral bases of LSHS and HSLS images by making full use of the rich spectral information in LSHS data. The spectral bases of these two categories data then formulate a dictionary-pair due to their correspondence in representing each pixel spectra of LSHS data and HSLS data, respectively. Subsequently, the LSHS image is spatial unmixed by representing the HSLS image with respect to the corresponding learned dictionary to derive its representation coefficients. Combining the spectral bases of LSHS data and the representation coefficients of HSLS data, fused data are finally derived which are characterized by the spectral resolution of LSHS data and the spatial resolution of HSLS data. The experiments are carried out by comparing the proposed method with two representative methods on both simulation data and actual satellite images, including the fusion of Landsat/ETM+ and Aqua/MODIS data and the fusion of EO-1/Hyperion and SPOT5/HRG multispectral images. By visually comparing the fusion results and quantitatively evaluating them in term of several measurement indices, it can be concluded that the proposed method is effective in preserving both the spectral information and spatial details and performs better than the comparison approaches.     

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.     

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.     

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.     

Fully spatially adaptive smoothing parameter estimation for Markov random field super-resolution mapping of remotely sensed images

This article presents a fully spatially adaptive Markov random field (MRF)-based super-resolution mapping (SRM) technique to produce land-cover maps at a finer spatial resolution than the original coarse-resolution image. MRF combines the spectral and spatial energies; hence, an MRF-SRM technique requires a smoothing parameter to manage the contributions of these energies. The main aim of this article is to introduce a new method called fully spatially adaptive MRF-SRM to automatically determine the smoothing parameter, overcoming limitations of the previously proposed approaches. This method estimates the number of endmembers in each image and uses them to assess the proportions of classes within each coarse pixel by a linear spectral unmixing method. Then, the real pixel intensity vectors and the local properties of each coarse pixel are used to compute the local spectral energy change matrix and the local spatial energy change matrix for each coarse pixel. Each pair of matrices represents all possible situations in spatial and spectral energy change for each coarse pixel and can be used to examine the balance between spatial and spectral energies, and hence to estimate a smoothing parameter for each coarse pixel. Thus, the estimated smoothing parameter is fully spatially adaptive with respect to real pixel spectral vectors and their local properties. The performance of this method is evaluated using two synthetic images and an EO1-ALI (The Advanced Land Imager instrument on Earth Observing-1 satellite) multispectral remotely sensed image. Our experiments show that the proposed method outperforms the state-of-the-art techniques.     

非负矩阵分解融合高光谱和多光谱数据

由于光谱分辨率和空间分辨率的制约以及物理条件的限制,高光谱数据具有很高的光谱分辨率而其空间分辨率却很低。因此,一般高光谱数据的空间分辨率往往低于仅有几个波段的多光谱数据的空间分辨率。高光谱数据和多光谱数据的融合可以得到同时具有高空间分辨率和高光谱分辨率的数据,进而应用于更高空间分辨率下地物的识别和分类。非负矩阵分解(Nonnegative Matrix Factorization)算法用于实现低空间分辨率高光谱数据和高空间分辨率多光谱数据的融合。首先利用顶点成分分析法VCA(Vertex Component Analysis)分解高光谱数据,得到初始的端元波谱矩阵和端元丰度矩阵;然后用非负矩阵分解算法交替地对高光谱数据和多光谱数据进行分解,得到高光谱分辨率的端元波谱矩阵和高空间分辨率的丰度矩阵;最后两个矩阵相乘得到高空间分辨率和高光谱分辨率的融合结果。在每一步非负矩阵分解过程中,数据之间的传感器观测模型用于分解矩阵的初始化。AVIRIS和HJ-1A数据实验结果分析表明:非负矩阵分解算法有效提高了高光谱数据的所有波长范围内波段数据的空间分辨率,而高精度的融合结果可用于地物的目标识别和分类。     

基于双正交多小波变换的多光谱与全色图像融合研究

针对多光谱与全色图像融合中存在的光谱扭曲问题,提出了一种利用双正交多小波进行多分辨率分析,并结合平均与选择法处理小波高频系数的融合算法。该算法首先对已配准的多光谱图像进行IHS变换,然后分别对变换得到的强度分量I与全色图像进行双正交多小波分解,为增强融合图像的空间信息,对分解得到的高频系数利用平均与选择相结合的方法来确定,低频系数则通过邻域方差准则得到。最后由新的小波低频和高频系数重构并进行IHS逆变换得到融合图像。实验结果表明,该方法可以有效减少光谱扭曲,并提高图像的空间分辨率,保留图像中的边缘细节。     

An adaptive spatially constrained fuzzy c-means algorithm for multispectral remotely sensed imagery clustering

This paper presents a novel adaptive spatially constrained fuzzy c-means (ASCFCM) algorithm for multispectral remotely sensed imagery clustering by incorporating accurate local spatial and grey-level information. In this algorithm, a novel weighted factor is introduced considering spatial distance and membership differences between the centred pixel and its neighbours simultaneously. This factor can adaptively estimate the accurate spatial constrains from neighbouring pixels. To further enhance its robustness to noise and outliers, a novel prior probability function is developed by integrating the mutual dependency information in the neighbourhood to obtain accurate spatial contextual information. The proposed algorithm is free of any experimentally adjusted parameters and totally adaptive to the local image content. Not only the neighbourhood but also the centred pixel terms of the objective function are all accurately estimated. Thus, the ASCFCM enhances the conventional fuzzy c-means (FCM) algorithm by producing homogeneous regions and reducing the edge blurring artefact simultaneously. Experimental results using a series of synthetic and real-world images show that the proposed ASCFCM outperforms the competing methodologies, and hence provides an effective unsupervised method for multispectral remotely sensed imagery clustering.     

提升五株形小波在多光谱图像融合中的应用

提出一种基于IHS变换和提升五株形小波变换相结合的融合方法,并把它应用于多光谱图像与高分辨图像的融合中。该算法对多光谱图像进行IHS变换,将得到的亮度分量I和高分辨率图像做多尺度提升五株形小波分解,采用不同的融合算子对高低频分量进行融合,对融合后图像进行提升五株形小波重构和IHS逆变换得到融合结果图像,并采用客观性能指标对融合结果图像进行了客观评价。实验结果表明,该方法对多光谱图像和高分辨率图像的融合有较好的融合效果,能从原图像中获得更多的信息,同时又能保持较高的空间分辨率。该方法的融合算法和分解层数的选取,是简便有效的,适用于多光谱图像融合。     

Research on ALI Image Fusion Algorithm based on Improved Brovey Transform

Aiming at the problems of spectral information loss and spectral distortion in traditional Brovey Transform (BT) fusion, the adaptive weighted average is introduced to improve it. Taking EO-1 ALI multispectral imagery as an example, a new multispectral image fusion algorithm based on improved BT is proposed. Information entropy, average gradient, correlation coefficient and root mean square error are used to comprehensively evaluate and compare the fusion effects of this algorithm, so as to verify the effectiveness and superiority of the multispectral image fusion algorithm based on improved BT. Experimental results show the multispectral fusion image using this improved algorithm has better spectral information and spatial resolution, and its visual effects and spatial texture features have been significantly improved, and the color information of the source image has been well extended, and the brightness is relatively moderate; this improved algorithm can reduce the loss and distortion of spectral information in the fusion process, and has obvious advantages in maintaining spectral information and clarity compared with the traditional BT fusion method.     

基于改进Brovey变换的ALI影像融合算法研究

针对传统Brovey变换（Brovey Transform,BT）融合存在的光谱信息丢失及光谱扭曲等问题,引入自适应加权平均对其进行改进,并以EO-1 ALI多光谱影像为例,提出一种新的基于改进BT的多光谱影像融合算法。并分别选用信息熵、平均梯度、相关系数、均方根误差等参数对影像融合效果进行综合评价与对比分析,从而验证该算法的有效性和优越性。结果显示：采用改进后的BT融合算法的多光谱融合影像具有较好的光谱信息和空间分辨率,其视觉效果和空间纹理特征都有显著改善,并且较好地延续了源影像的色彩信息,亮度相对适中;能减少融合过程中光谱信息的损失和畸变,在保持光谱信息和清晰度方面与传统BT融合算法相比具有明显优势。     

Smoothing Filter-based Intensity Modulation: A spectral preserve image fusion technique for improving spatial details

Image fusion techniques are widely used to integrate a lower spatial resolution multispectral image with a higher spatial resolution panchromatic image, such as Thematic Mapper (TM) multispectral band and SPOT Panchromatic images. However, the existing techniques either cannot avoid distorting the image spectral properties or involve complicated and time-consuming frequency decomposition and re-construction processing. A simple spectral preserve fusion technique: the Smoothing Filter-based Intensity Modulation (SFIM) has thus been developed based on a simplified solar radiation and land surface reflection model. By using a ratio between a higher resolution image and its low pass filtered (with a smoothing filter) image, spatial details can be modulated to a co-registered lower resolution multispectral image without altering its spectral properties and contrast. The technique can be applied to improve spatial resolution for either colour composites or individual bands. The fidelity to spectral property and the spatial textural quality of SFIM are convincingly demonstrated by an image fusion experiment using TM and SPOT Panchromatic images of south-east Spain. The visual evaluation and statistical analysis compared with HSI and Brovey transform techniques confirmed that SFIM is a superior fusion technique for improving spatial detail of multispectral images with their spectral properties reliably preserved.     

基于能量最小化的图像融合

图像融合是指联合两个或两个以上的图像通过某种算法得到一幅更高质量的新图像.提出了一种融合全色图像和光谱图像的方法即能量最小化方法,能量主要由两个部分组成.第1部分保证了相关细节信息的注入.第2部分保持了多光谱图像的低频信息.另外,能量还可以包含高分辨率光谱图像的先验知识和其它一些约束条件.     

通过直方图中轴化提高融合图像光谱保真度

针对遥感图像融合过程中光谱失真问题,提出一种基于直方图中轴化策略的图像融合算法。首先,将多光谱图像进行IHS变换;然后,采用直方图中轴化策略调整多光谱图像强度分量图像和全色图像的像素直方图,使之趋于一致;最后,进行IHS反变换获得高质量的彩色图像。理论分析和实验结果表明,该算法不仅可以较好地抑制融合图像光谱失真,同时也能有效保留融合图像的空间分辨率,算法步骤简单、容易实现;与四种传统融合算法（IHS变换、主成分分析（PCA）法、小波变换（WT）法、Brovey）相比,该算法生成的融合图像具有良好的视觉效果,特别是在峰值信噪比（PSNR）、光谱扭曲度和信息熵等客观评价指标中明显优于对比算法。基于直方图中轴化策略融合的遥感图像光谱失真度小、空间信息保持度高。     

A novel adaptive fast IHS transform fusion method driven by regional spectral characteristics for Gaofen-2 imagery

ABSTRACT

With the increasing diversity of applications based on the Gaofen-2 satellite imagery, broadly applicable methods to generate high quality fused images is a significant problem to investigate. To obtain an image with high spatial and spectral resolutions from given panchromatic (Pan) images and multispectral (MS) images, most existing fusion algorithms adopt a unified strategy for the whole image. However, regions have distinct characteristics that impact the spatial and spectral resolution processing, on account of their varying regional features. In this article, to satisfy the diverse needs of different regions, a novel fast IHS (Intensity-Hue-Saturation) transform fusion method driven by regional spectral characteristics is proposed to fuse Gaofen-2 imagery. First, by the fast IHS transform framework, the original intensity component is obtained from the upsampled MS imagery. Then, numerous independent regions of upsampled MS imagery are generated by a novel superpixel merging strategy, and the spectral characteristics of these regions are utilized for generating a fusion factor. Next, to acquire a new fused intensity component, the fusion factor is applied to guide the injection of details in the fusion procedure. This fusion factor adapts the method to meet the spatial and spectral resolution needs for each region. Finally, the difference between the new fused intensity component and the original one is regarded as the detail that needs to be injected; these are added equally to the different bands of the upsampled MS imagery to yield the final fused multispectral image. In comparison with other classical algorithms, the visual and statistical analysis reveal that our proposed method can provide better results in improving spatial detail and preserving spectral information.     

多光谱图像与全色图像的像素级融合研究

以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双源融合为例，详细地总结了卫星多源遥感图像融合领域像素级融合的步骤、基本融合模型和优缺点，重点分析了各种常见像素级融合方法的原理和特点，并归纳了像素级融合结果的主客观评价标准和评价方法，以及像素级融合的主要应用领域，最后讨论了像素级融合目前存在的问题和今后的发展方向。