基于区域特征加权的IHS图像融合方法

针对传统IHS变换图像融合方法在高分辨率和多光谱图像融合中存在的光谱畸变现象,研究了现有的一些改进IHS变换图像融合方法,提出了一种基于局部区域多元特征动态加权的IHS图像融合方法,根据主观目视判决和客观评价指标对融合结果进行了比较和分析。仿真结果表明,该文提出的方法能使融合图像具有较好的空间分辨率,同时又能很好地保持原有多光谱图像的光谱特性。     

基于视觉注意机制与区域结构相似度的图像融合质量评价

针对目前图像融合质量客观评价与主观评价结果一致性不高的问题,结合人类视觉注意机制和区域计算方法对EFQI指标进行改进,提出了一种基于视觉注意机制（VAM）与区域结构相似度的图像融合质量评价指标。该指标充分考虑了人类视觉感兴趣区域和人眼对区域信息敏感的特性,使用方差显著图与视觉显著图相结合的加权方法对图像中的显著区域赋予更大的权值,并采用更符合人类视觉特性的区域计算方法求取融合结果图像和源图像在各区域上的结构相似度以评价融合质量。用该指标评价10种不同融合算法的融合结果图像并与主观评价结果进行相关性分析,分析结果表明该方法相比传统的评价指标能更有效地反映融合图像质量,与主观评价结果一致性更高。     

基于加权改进小波变换的图像融合算法

针对低对比度图像融合时会造成图像细节模糊等缺陷,提出一种基于加权双正交自适应小波变换的图像融合算法。取小波系数局部模极大并进行自适应加权修正以融合高频系数。采用对2幅原图像的低频系数加权自适应的方法对低频系数进行融合。对多聚焦图像和多光谱彩色图像分别采用计算图像信息熵和均方根误差、计算图像平均梯度的方法对融合的性能进行评价。实验结果表明,采用该融合规则得到的融合图像具有良好的融合效果。     

基于加权相关矩的多传感器图像融合方法

针对遥感多光谱图像和全色图像的融合问题,提出了一种基于局部区域特征的多分辨率图像融合方法。该方法首先对图像进行二维小波变换,然后根据高频小波系数的一阶、二阶统计特征来计算加权相关矩,从而得到包含更多信息和有效特征的融合图像。试验结果证明融合图像在保留地物光谱信息和提高空间细节表现能力上都具有很好的效果。     

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.     

基于区域特性选择的遥感图像融合方法

提出了一种新的遥感图像融合方法,其基本思想是首先对多光谱遥感图像(MS)进行IHS变换得到IHS色彩空间的I、H、S分量;然后将高分辨率全色图像和MS的I分量进行3层小波变换,并采取基于区域特性选择的融合算子对小波系数进行选择,然后经小波逆变换重构得到新的强度分量I;′最后将I′、H、S进行IHS逆变换得到融合后的图像。此外,文中引入了相关系数,相对平均光谱误差指数(RASE),相对整体维数综合误差(ERGAS)对融合图像的光谱质量进行评价,使用将融合图像与高分辨率全色图像分别经过拉普拉斯滤波器的滤波以提取细节信息,然后计算相关系数的方法对融合图像的空间细节质量进行评价。实验结果表明,本文算法在光谱质量的改善方面明显优于IHS及一些经典的小波变换遥感图像融合算法。     

TM多光谱与SPOT 全色遥感图像融合算法对比

融合技术是遥感数据处理中一种重要的方法。而TM多光谱与SPOT全色图像是遥感融合最为普遍的选择。为了对比分析不同方法在融合TM多光谱与SPOT全色图像上的效果,提出基于色彩空间的HSV变换、基于算数技术的Brovey变换和Gram—Schmidt波谱锐化3种融合方法相结合,实现了对同一传感器的全色和多光谱数据融合。试验表明:就空间信息量而言,经过HSV变换的图像具有最大的空间信息,但其光谱保真能力最差;Brovey变换最大限度保持了原始图像的光谱信息,而空间信息的详细程度较差;Gram-Schmidt波谱锐化后的影像不仅保持了多光谱影像的光谱信息,同时又保持了高光谱全色影像的空间细节信息,是一种较好的图像融合方法。     

相关系数准则下的多光谱图像最优融合方法

证明了在相关系数准则下最优融合算法与线性最优融合算法是等价的，利用这一结果大大减少了计算的空间与时间复杂度；并通过设置加权系数和提供一种自适应计算权值的方法以突出原多光谱图像目标区域的频谱特征；对于相关性较弱的多光谱图像集提出了分组融合的方法．实验结果表明：该最优融合算法高效可行，能处理各种不同多光谱场景的融合．     

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。     

遥感图像融合定量评价方法及实验研究

近年来融合技术不断创新,方法多种多样,但是对融合结果缺乏统一的定量评价方法。在分析与总结当前常用的遥感图像融合结果定量评价方法的基础上,给出了亮度信息、空间细节信息和光谱信息等定量评价参数,并且通过程序实现了定量评价。以IHS和PCA两种融合方法对QuickBird图像多光谱波段和全色波段融合作为实验,进行定量评价分析,结果表明所提出的定量评价参数能够较准确地反映图像融合情况,比定性评价有效、全面,可为选择恰当的融合方法提供依据。     

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.     

区域特征动态加权的IHS小波遥感图像融合

研究传统的IHS变换、小波变换和两者结合的图像融合方法,提出一种区域多元特征动态加权的IHS小波遥感图像融合算法。结合IHS变换和小波变换的优点,对匹配后的新全色分量和IHS变换后多光谱图像的亮度分量,采用多元特征动态加权法进行小波融合。实验结果表明,与传统融合算法相比,该算法在空间细节增强和光谱信息保持方面性能较优。     

Spectral or spatial quality for fused satellite imagery? A trade‐off solution using the wavelet à trous algorithm

Several different methods for the fusion of multispectral and panchromatic images based on the wavelet transform have been proposed. The majority provide satisfactory results, but there is one, the à trous algorithm, that presents several advantages over the other fusion methods. Its computation is very simple; it only involves elementary algebraic operations, such as products, differences and convolutions. It yields a better spatial and spectral quality than the other methods. Standard fusion methods do not allow control of the spatial and spectral quality of the fused image; high spectral quality implies low spatial quality and vice versa. This paper proposes a new version of a fusion method based on the wavelet transform, computed through the à trous algorithm, that permits customization of the trade‐off between the spectral and spatial quality of the fused image through the evaluation of two quality indices: a spectral index (the ERGAS index) and a spatial one. For the latter, a new spatial index based on ERGAS concepts and translated to the spatial domain has been defined. In addition, several different schemes for the computation of the fusion method investigated have been evaluated to optimize the degradation level of the source image required to perform the fusion process. The performance of the proposed fusion method has been compared with the fusion methods based on wavelet Mallat and filtering in the Fourier domain.     

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。     

Multispectral images fusion by a joint multidirectional and multiresolution representation

This paper proposes a new fusion method that permits an adequate selection of information extracted from source images to obtain fused images with good spatial and spectral quality simultaneously. This method is based on a joint multiresolution multidirectional representation of the source images using a single directional spatial frequency low pass filter bank of low computational complexity, defined in the Fourier domain. The source images correspond to those captured by the IKONOS satellite (panchromatic and multispectral). The results obtained indicate that the proposed method provides, in a simple manner, objective control over the trade‐off between high spatial and spectral quality of the fused images.     

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.     

ETM影像融合的评价分析

针对Landsat-7 ETM影像，对采用HIS变换、Brovey变换、相关系数加权法、SFIM变换、ISVR变换5种方法实现的ETM全色波段与ETM多光谱波段融合结果进行分析，并以均值、标准差、信息熵、平均梯度及相对偏差作为评价指标，通过试验对融合影像进行综合定量评价。结果表明，HIS变换光谱失真最大；Brovey变换在光谱保真和保持空间分辨率方面都不理想；相关系数加权法的融合结果严重地依赖权系数的取值；基于SFIM变换的融合方法，能最大限度地保留光谱特征，但空间分辨率提高不明显；而基于ISVR变换的融合方法，光谱失真小，空间分辨率高，其最大优势是考虑了多光谱影像与全色影像间的波段关系，可作为ETM影像融合的最佳方法。
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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