DCT域遥感影像融合算法

提出一种基于离散余弦变换(Discrete Cosine Transform,DCT)与IHS变换的多光谱与全色遥感影像融合方法及其改进算法。本文方法依据DCT系数的特点在DCT域进行遥感影像融合,适合压缩格式的遥感影像快速融合。目视效果与客观评价表明,相比常用遥感影像融合方法,本文方法能在提高空间分辨率与保持光谱特性之间得到更好的折衷。     

混沌蜂群优化的NSST域多光谱与全色图像融合

为有效融合多光谱图像的光谱信息和全色图像的空间细节信息,提出了一种基于混沌蜂群优化和改进脉冲耦合神经网络(PCNN)的非下采样Shearlet变换(NSST)域图像融合方法。首先对多光谱图像进行Intensity-HueSaturation(IHS)变换,全色图像的直方图按照多光谱图像亮度分量的直方图进行匹配;然后分别对多光谱图像的亮度分量和新全色图像进行NSST变换,对低频分量使用改进加权融合算法进行融合,以互信息作为适应度函数,利用混沌蜂群算法找到最优加权系数。对高频分量采用改进脉冲耦合神经网络(PCNN)方法进行融合,再经NSST逆变换和IHS逆变换得到融合图像。本文方法在主观视觉效果和信息熵、光谱扭曲度等客观定量评价指标上优于基于IHS变换、基于非下采样Contourlet变换(NSCT)和非负矩阵分解(NMF)、基于NSCT和PCNN等5种融合方法。本文方法在提升图像空间分辨率的同时,有效地保留了光谱信息。     

基于非下采样Contourlet变换的遥感影像融合

根据非下采样Contourlet变换的特点,提出一种基于非下采样Contourlet变换和IHS变换相结合的遥感影像融合方法.该方法首先对金色影像和多光谱影像经IHS变换后的I分量分别进行非下采样Contourlet 变换;然后采取不同的融合策略分别对高低频系数进行融合:低频系数采用区域能量加权的方法进行融合,高频系数则利用八邻域梯度优先的原则进行融合;最后通过非下采样Contourlet逆变换和IHS逆变换得到融合影像.实验结果表明,该方法在提高融合影像空间分辨率的同时,能更好地保持影像的光谱质量.     

A GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation

High spatial resolution and spectral fidelity are basic standards for evaluating an image fusion algorithm. Numerous fusion methods for remote sensing images have been developed. Some of these methods are based on the intensity–hue–saturation (IHS) transform and the generalized IHS (GIHS), which may cause serious spectral distortion. Spectral distortion in the GIHS is proven to result from changes in saturation during fusion. Therefore, reducing such changes can achieve high spectral fidelity. A GIHS-based spectral preservation fusion method that can theoretically reduce spectral distortion is proposed in this study. The proposed algorithm consists of two steps. The first step is spectral modulation (SM), which uses the Gaussian function to extract spatial details and conduct SM of multispectral (MS) images. This method yields a desirable visual effect without requiring histogram matching between the panchromatic image and the intensity of the MS image. The second step uses the Gaussian convolution function to restore lost edge details during SM. The proposed method is proven effective and shown to provide better results compared with other GIHS-based methods.     

基于经验模态分解的高分辨率影像融合

文章提出基于经验模态分解（Emp iricalMode Decomposition,EMD）的特征层影像融合模型。对多光谱波段影像进行IHS变换获得强度影像,采用行列分解实现一维经验模态分解的二维拓展,并用于分离高分辨波段影像与强度影像的细节特征信息,对高分辨率波段影像的高频与强度影像波段的低频进行重构获得融合后的强度影像,再通过IHS反变换获得融合影像。文章介绍了经验模态分解的基本原理,定义了经验模态分解的多尺度分解与合成结构,提出融合模型的技术路线。选择UICKB IRD影像的全色波段与多光谱波段进行融合实验,根据典型行（列）的EMD分析,确定经验模量的取舍尺度,按提出的融合路线获得融合影像,并与小波融合,IHS融合,Brovey融合模型获得的影像进行视觉及量化比较。选择信息熵、标准差指标对融合影像的空间细节信息进行评价,同时选择平均灰度值、相关系数、偏差指数评价融合影像的光谱扭曲程度,结果表明本融合模型最优。     

基于区域统计特性的IHS-小波遥感影像融合

在遥感影像融合过程中,为了更好地避免经典IHS算法造成的光谱扭曲,文章提出了一种基于统计特性的IHS和提升小波结合的影像融合方法。该方法对小波变换后的1分量和高分辨率影像的高频和低频系数,根据不同的区域统计特性分别选取不同的融合策略进行融合,然后进行小波逆变换和色彩空间转化,得到融合影像。实验表明,此方法与IHS变换、高通滤波法和小波变换等传统算法相比,在提高多光谱影像空间分辨率的同时,能更好地保持影像的光谱质量。     

SPOT-7遥感图像融合技术对比研究

随着高分辨率影像研究的发展,影像融合成为遥感影像处理领域中一个重要的研究方向。目前对于影像的融合已有很多的方法,如IHS法、HPF法等。本文将以SPOT-7高分辨率影像作为研究对象,探讨经PCA法、Pansharpening法、Gram-Schmidt法、NNDiffuse法这4种融合方法,融合后的影像在保持地物的波谱特征,以及信息识别效果等多方面的各自优势,并通过定性和定量评价来确定适合SPOT-7高分辨率影像的融合方法,实验结果表明:NNDiffuse方法的融合结果最为理想。     

一种基于小波系数特征的遥感图像融合算法

多光谱图像和全色图像是目前卫星遥感领域最常见的传感器图像.为了更充分地发挥这两类遥感图像数据的价值,人们利用两类数据的互补性,将多传感器融合技术引进了遥感图像处理领域.在IHS彩色空间变换和小波多分辨率分析的基础上,利用图像高频小波系数的多个特征来定义特征量积,并利用特征量积作为依据提出了一种图像融合新算法.通过一组多光谱图像和全色图像数据进行融合仿真试验,并将该算法与IHS,HPF等算法和归一化矩算法作了比较.证明该方法能在保留多光谱图像光谱信息的基础上,有效地提高多光谱图像的空间分辨率.     

Remote Sensing Image Fusion Based on Nonlinear IHS and Fast Nonsubsampled Contourlet Transform

The purpose of remote sensing image fusion is to inject the detail image extracted from the panchromatic (PAN) image into the low spatial resolution multispectral (MS) image. A novel remote sensing image fusion method based on fast nonsubsampled contourlet transform (FNSCT) and Nonlinear intensity-hue-saturation (IHS) is presented in this paper. Firstly, the Nonlinear IHS transform is performed on the multispectral image, and then the I-component representing the spatial resolution and the panchromatic image is transformed by NSCT to obtain the low frequency and high frequency. Finally, the coefficients are selected using the improved sum-modified-Laplacian (SML) method and the improved Log-Gabor filter in the low frequency and the high frequency, respectively. Experimental results show that the proposed method is the most advanced fusion method in subjective and objective evaluation, can provide more spatial information, and retain more spectral information compared with several other methods.     

SIFT-FANN: An efficient framework for spatio-spectral fusion of satellite images

Image fusion techniques are widely used for remote sensing data. A special application is for using low resolution multi-spectral image with high resolution panchromatic image to obtain an image having both spectral and spatial information. Alignment of images to be fused is a step prior to image fusion. This is achieved by registering the images. This paper proposes the methods involving Fast Approximate Nearest Neighbor (FANN) for automatic registration of satellite image (reference image) prior to fusion of low spatial resolution multi-spectral QuickBird satellite image (sensed image) with high spatial resolution panchromatic QuickBird satellite image. In the registration steps, Scale Invariant Feature Transform (SIFT) is used to extract key points from both images. The keypoints are then matched using the automatic tuning algorithm, namely, FANN. This algorithm automatically selects the most appropriate indexing algorithm for the dataset. The indexed features are then matched using approximate nearest neighbor. Further, Random Sample Consensus (RanSAC) is used for further filtering to obtain only the inliers and co-register the images. The images are then fused using Intensity Hue Saturation (IHS) transform based technique to obtain a high spatial resolution multi-spectral image. The results show that the quality of fused images obtained using this algorithm is computationally efficient.     

Improved IHS Algorithm for Fusing High Resolution Satellite Images of Urban Areas

The intensity-hue-saturation method is used frequently in image fusion due to its efficiency and high spatial quality. The main shortage is its spectral distortion stemmed from replacement of intensity band with higher resolution image. In this study, a new method is introduced to improve the spectral quality of the Intensity-Hue-Saturation (IHS) algorithm. The goal of this study is to produce the fused image that has a better spectral and spatial quality with respect to the original images in term of visual comparison and the classification result. In this regard, an improved statistical approach is developed to combine an intensity band from IHS algorithm and an input high resolution image such as SAR or Panchromatic image. Then the intensity image is replaced by the combined image band. Final fused images are attained using the inverse IHS algorithm. The proposed fusion algorithm is tested on two data sets of: a) panchromatic and multi spectral bands of IKONOS image with the same acquisition date, and b) multi spectral and HH bands of IKONOS and TerraSAR-X images respectively with different acquisition dates. Moreover, the obtained results are compared with other fusion methods like IHS, Gungor, Brovey and synthetic variable ratio. The results show less spectral discrepancy of the proposed method comparing to other methods. Finally, the outcome of proposed method is classified and classification overall accuracy is improved by 5.6 and 2 percentage for data set ‘a’ and ‘b’ respectively.     

Fusion of multispectral and panchromatic Satellite images using the curvelet transform

A useful technique in various applications of remote sensing involves the fusion of different types of satellite images, namely multispectral (MS) satellite images with a high spectral and low spatial resolution and panchromatic (Pan) satellite image with a low spectral and high spatial resolution. Recent studies show that wavelet-based image fusion provides high-quality spectral content in fused images. However, the results of most wavelet-based methods of image fusion have a spatial resolution that is less than that obtained via the Brovey, intensity-hue-saturation, and principal components analysis methods of image fusion. We introduce an improved method of image fusion which is based on the amelioration de la resolution spatiale par injection de structures (ARSIS) concept using the curvelet transform, because the curvelet transform represents edges better than wavelets. Because edges are fundamental in image representation, enhancing the edges is an effective means of enhancing spatial resolution. Curvelet-based image fusion has been used to merge a Landsat Enhanced Thematic Mapper Plus Pan and MS image. The proposed method simultaneously provides richer information in the spatial and spectral domains.     

一种基于IHS圆柱变换模型的影像融合质量综合评价方法

影像融合在遥感影像分析和处理中有着广泛的应用,而针对影像融合的质量评价亦已成为国内外的研究热点.在现有的融合评价算法中,大部分定量评价指标没有统一的评价基准,无法同时对融合影像的空间特性和光谱特性这一对矛盾特性进行综合评价.本文采用与传统方法不同的思路,在分析常用融合模型的基础上,经理论推导得到一种“基于IHS圆柱变换...     

基于IHS变换与多进制小波变换的多源遥感影像融合

不同来源的遥感数据融合已经成为制约遥感数字图像处理技术发展的重要因素,文中基于IHS变换与改进的多进制小波变换相结合,对多源遥感图像进行数据融合,使融合后的数据既减少了IHS变换引起的图像光谱失真现象,又具有高空间分辨率的优点,同时也可广泛应用其它领域。     

不同遥感影像融合方法的质量评价

近年来,随着遥感技术的迅猛发展,多源遥感影像数据越来越多,有效利用这些海量遥感数据是遥感领域的一大热门话题,同时,对融合后的遥感影像进行质量评价使融合处理过程得到优化,也是影像融合中必不可少的一个重要环节。重点利用ERDAS软件,分别采用Brovey变换法、IHS变换法、高通滤波法以及主成分分析法对影像进行融合实验,并对融合后的影像进行主观评价;采用Matlab编写程序,提取了融合影像的定量评价指标均值、标准差、平均梯度、信息熵和相关系数,完成了对融合影像的客观评价;最后对融合后的遥感影像进行了对比分析,得出主成分分析法的影像在光谱保持特性上最好,Brovey法在信息量的保持和清晰度上都优于其他的融合方法,而通过IHS变换法的融合影像与原始影像的相关性最好的结论。     

基于多进制小波变换的遥感影像融合

首先介绍了遥感影像融合的理论和方法 ,然后在讨论多进制小波理论的基础上 ,提出了一种基于特征的多进制小波变换的影像融合算法 ,该算法根据待融合影像分辨率之比确定采用多进制小波 ,从而最大限度的利用了待融合影像的信息 ,防止影像信息的丢失。通过对具体影像的实验 ,证明融合后的影像最大限度地保留了待融合影像的光谱信息 ,同时提高了待融合影像的清晰度和空间分辨率。文中给出了SPOT全色影像与SPOT多光谱波段影像、SPOT全色影像与TM影像的融合结果 ,并与其他方法进行了比较 ,证明了本方法的优越性和自适应能力     

一种基于Choquet模糊积分小波系数选择的遥感图像融合方法

为较好地保留多光谱、高分辨率遥感影像融合时的光谱信息和高分辨率信息,在小波变换基础上提出了一种基于模糊积分的融合算法,其基本思想是在IHS空间,对强度分量Ⅰ及高分辨率图像进行小波分解后,保留Ⅰ分量的低频系数,对于高频细节特征,将局部窗口内的方差、平均梯度和能量当作单因素指标,应用Choquet模糊积分综合各单因素指标得到一个综合指标,并据此来选取小波系数.实验结果表明,该算法在光谱质量改善方面明显优于IHS以及一些经典的小波变换遥感图像融合算法,是一种有效的遥感图像融合算法.     

多源高分辨率遥感影像智能融合

本文面向多源高分辨率遥感影像自动化融合的应用需求,探索按需应用的智能化融合方法,充分利用不同分辨率和不同时相的高分辨率多源遥感影像数据资源与特性,研究了影像融合数据源选取的决策树算法,建立了遥感影像融合规则知识库,并自动化选取适合的融合算法,提出了Curvelet_HCS算法,对低频和高频系数选用不同的融合规则,改善了HCS算法的光谱失真问题,可同时融合多光谱影像的多个谱段,并保持更丰富的空间细节信息。根据融合评价结果对遥感影像融合规则知识库进行更新,实验验证表明了该套方法的有效性,为开展大规模智能化的多源遥感影像融合应用提供了重要的方法和技术支撑。     

基于IHS变换的最优树分解在遥感图像融合中的应用

探讨了遥感多光谱与全色波段图像的融合问题，分析了基于IHS变换的小波包变换分解的遥感图像融合方法，提出了基于最优树分解的融合方法。此方法首先将多光谱图像进行IHS变换，然后对I分量和全色图像进行小波包分解和最优树分解，再进行融合，最后进行IHS 逆变换得到融合图像。此方法不仅得到较好的图像主观视觉效果，而且兼顾了客观上熵最大的原则。     

基于经验模态分解的高分辨率影像融合(英文)

High resolution image fusion is a significant focus in the field of image processing. A new image fusion model is presented based on the characteristic level of empirical mode decomposition （EMD）. The intensity hue saturation （IHS） transform of the multi-spectral image first gives the intensity image. Thereafter, the 2D EMD in terms of row-column extension of the 1D EMD model is used to decompose the detailed scale image and coarse scale image from the high-resolution band image and the intensity image. Finally, a fused intensity image is obtained by reconstruction with high frequency of the high-resolution image and low frequency of the intensity image and IHS inverse transform result in the fused image. After presenting the EMD principle, a multi-scale decomposition and reconstruction algorithm of 2D EMD is defined and a fusion technique scheme is advanced based on EMD. Panchromatic band and multi-spectral band 3,2,1 of Quickbird are used to assess the quality of the fusion algorithm. After selecting the appropriate intrinsic mode function （IMF） for the merger on the basis of EMD analysis on specific row （column） pixel gray value series, the fusion scheme gives a fused image, which is compared with generally used fusion algorithms （wavelet, IHS, Brovey）. The objectives of image fusion include enhancing the visibility of the image and improving the spatial resolution and the spectral information of the original images. To assess quality of an image after fusion, information entropy and standard deviation are applied to assess spatial details of the fused images and correlation coefficient, bias index and warping degree for measuring distortion between the original image and fused image in terms of spectral information. For the proposed fusion algorithm, better results are obtained when EMD algorithm is used to perform the fusion experience.