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。     

Spectral and Spatial Quality Analysis in Pan Sharpening Process

Image fusion is a process to obtain new images containing more information by combining images obtained same or different sensors. With most of the earth observation satellites, high spatial resolution panchromatic images and low spatial resolution multispectral images are obtained. As an example of image fusion ??pan sharpening?? is a process of combining of high spatial resolution panchromatic images and low spatial resolution multispectral images. At the end of the fusion process both high spatial and spectral resolution new images are obtained. In this study, panchromatic and multispectral images gathered from Ikonos were used. Panchromatic and multispectral images belonging to the same sensor were combined by using different image fusion methods. As pan sharpening methods Brovey transform, Modified IHS, Principal Component Analysis (PCA), Wavelet PC transform and Wavelet A Trous transformation methods were used. Quality of fused products was evaluated from the point of view of both visual and statistical criteria. While wavelet based methods are succesfull in terms of protection of spectral quality of original multispectral images, the colorbased and statistical methods are giving better results within the improvement of spatial content.     

基于小波包变换与IHS变换的遥感图像融合

针对多光谱图像与金色图像的融合，提出了基于IHS变换和小波包变换的遥感图像融合新方法。该方法首先对多光谱图像作IHS变换得到3个分量：亮度I、色度H和饱和度S；其次利用小波包变换融合方法融合多光谱图像的亮度分量与金色图像，并用融合后的图像替代多光谱图像的亮度分量；最后作IHS反变换得到新的多光谱图像。实验分析表明，新方法的性能优于IHS变换融合方法、小波变换融合方法和基于小波变换与IHS变换融合方法，在保留多光谱图像光谱信息的基础上，增强了融合图像的空间细节表现能力。     

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。     

Comparison of Fusion Techniques for Very High Resolution Data for Extraction of Urban Land-Cover

This paper aims in presenting a thorough comparison of performance of pan sharpening techniques, belonging to spatial, spectral, scale-space and spatial-frequency domain, for Very High Resolution Satellite Data. With the availability of new Very High Resolution sensors, especially, World View-2 sensor, which provides data at sub-meter level, the need for fusion of Panchromatic (PAN) and Multispectral (MS) images has to be further investigated. Pan-sharpening techniques namely, Hue-Saturation-Intensity, Brovey Transform, Principal Components Analysis, Discrete Wavelet Transform, Stationary Wavelet Transform, Non Sub-sampled contourlet Transform and Pseudo Wigner Distribution (PWD) fusion method have been selected for the fusion of PAN and MS images of World View-2 sensor. Further, the comparison of performance of each of the techniques have been carried out by using various evaluation indicators, such as, Root Mean Square Error, Peak Signal-to-Noise Ratio, Correlation Coefficient, Universal Image Quality Index. It is found that PWD based fusion technique gives good result with a good trade-off between the preservation of spectral information and enhancement of spatial resolution.     

基于分辨率退化模型的全色和多光谱遥感影像融合方法

从影像成像的频率特性出发，提出了一种影像分辨率退化模型，并在此基础上提出了一种新的全色和多光谱遥感影像融合方法。     

Comparison of Pixel and Object Oriented Based Classification of Hyperspectral Pansharpened Images

In this paper pixel-based and object-oriented classifications were investigated for land-cover mapping in an urban area. Since the image fusion methods are playing a useful role in supplying classification different fusion approaches such as Gram-Schmidt Transform (GS), Principal Component Transform (PC), Haar wavelet, and À Trous Wavelet Transform (ATWT) algorithms have been used and the fused image with the best quality has been assessed on its respected classification. A Hyperion image and IRS-PAN image covering a region near Tehran, Iran have been used to demonstrate the enhancement and accuracy assessment of fused image over the initial images. The evaluation results of fused images showed that the Haar wavelet approach has good quality in preserving spectral information as well as spatial information. Classification results were compared to evaluate the effectiveness of the two classification approaches. Result of the pan-sharpened image classifications displayed that the object-oriented procedure presented more accurate outcomes (90.47 %) than those obtained by pixel-based classification method (77.33 %).     

影像局部直方图匹配滤波技术用于遥感影像数据融合

本文提出了一种基于局部直方图匹配波技术的影像融合新方法。该方法分析了局部影像统计特性,应用均值或均值－方差匹配正态函数,对要融合的两幅影像局部直方图进行匹配。文中给出了３种不同的融合算法（高通滤波法,局部均值匹配法和局部均值与方差匹配法）用于ＳＰＯＴ全色影像和ＴＭ多光谱影像融合的结果,并进行了比较,质量评价结果证明了提出的方法在提高了原ＴＭ多光谱影像空间分辨率的同时,较好地保留了多光谱影像频道的光     

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.     

A new Pansharpening Approach Based on NonSubsampled Contourlet Transform Using Enhanced PCA Applied to SPOT and ALSAT-2A Satellite Images

The Pansharpening process aims to merge the high spatial resolution of the panchromatic (Pan) image with the spectral information of the multispectral (MS) images. The fused images should represent an enhanced spatial resolution and should preserve the spectral information simultaneously. In the two last decades, many pansharpening algorithms have been implemented in the literature such as IHS, PCA, HPF, etc. Therefore, in comparison with the various conventional methods, our contribution is the conception of a new fusion scheme by combining two different approaches: the Principal Component Analysis (PCA) and the NonSubsampled Contourlet Transform (NSCT). The hypothesis in this combination represent the use of PCA, in first, like statistical approach to obtain from the MS bands the main information, followed by the NSCT as a robust multiresolution and multidirectional approach, to give an optimal representation of the characteristics in the image compared to the classical methods (wavelets), in order to overcome the drawback caused by PCA with the spectral distortion. The focus of this study is to show a new way to combine differently from usual those two approaches, to find a compromise between enhancing the spatial resolution and preserving the spectral information at the same time. The quality of the resulted images has been evaluated by the visual interpretation and the statistical assessment to prove its efficiency compared to other conventional methods.     

Multiresolution and Multispectral Data Fusion Using Discrete Wavelet Transform with IRS Images: Cartosat-1, IRS LISS III and LISS IV

Image fusion techniques integrate complimentary information from multiple image sensor data such that the new images are more suitable for the purpose of human visual perception and computer based processing tasks for extraction of detail information. As an important part of image fusion algorithms, pixel-level image fusion can combine spectral information of coarse resolution imagery with finer spatial resolution imagery. Ideally, the method used to merge data sets with high-spatial and highspectral resolution should not distort the spectral characteristics of the high-spectral resolution data. This paper describes the Discrete Wavelet Transform (DWT) algorithm for the fusion of two images using different spectral transform methods and nearest neighbor resampling techniques. This research paper investigates the performance of fused image with high spatial resolution Cartosat-1(PAN) with LISS IV and Cartosat-1(PAN) sensor images with the LISS III sensor image of Indian Remote Sensing satellites. The visual and statistical analysis of fused images has shown that the DWT method outperforms in terms of Geometric, Radiometric, and Spectral fidelity.     

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.     

基于小波包变换区域方差的遥感影像融合

介绍了基于小波包变换和区域方差的遥感影像融合方法.利用IHS变换和小渡包变换把全色影像和多光谱影像的相应分量分解为低频部分和高频部分,并分别采用加权平均法和区域方差法融合低频部分和高频部分,然后通过小波包重构和IHS逆变换得到最终的融合影像;最后采用MATLAB语言实现了这种方法.实验结果表明,这种方法在提高影像的清晰度、突出影像细节信息以及保留原始影像的光谱特征方面效果较好.     

IHS变换和小波变换相结合的遥感影像融合

本文针对低分辨率多光谱影像与高分辨率全色影像的融合，提出了一种IHS变换和小波变换相结合的遥感影像融合方法。方法首先对多光谱影像作IHS正变换，得到亮度I、色度H和饱和度S三个分量：然后利用小波变换融合方法，融合多光谱影像的亮度分量与全色影像，并用融合后的影像替代多光谱影像的亮度分量；最后，利用IHS反变换得到新的多光谱影像。试验结果分析表明，新方法的性能优于IHS变换融合方法、小波变换融合方法，在增强融合影像的空间细节表现能力的同时，很好地保留了多光谱影像的光谱信息。     

不同遥感影像融合方法在地理国情普查中的应用对比研究——以海南北部地区为例

利用比值变换融合、高通滤波融合、改进IHS融合、小波变换融合和基于主分量变换等图像融合方法对IKONOS多光谱影像和IKONOS全色影像进行融合,结合地理国情普查的地表覆盖类型,对比融合后影像的特点,并分析评价对比结果,为地理国情普查工作选用合适的融合方法生成效果较好、地物清晰的遥感底图,从而为提高解译效率提供参考依据。     

FFT-enhanced IHS transform method for fusing high-resolution satellite images

Existing image fusion techniques such as the intensity–hue–saturation (IHS) transform and principal components analysis (PCA) methods may not be optimal for fusing the new generation commercial high-resolution satellite images such as Ikonos and QuickBird. One problem is color distortion in the fused image, which causes visual changes as well as spectral differences between the original and fused images. In this paper, a fast Fourier transform (FFT)-enhanced IHS method is developed for fusing new generation high-resolution satellite images. This method combines a standard IHS transform with FFT filtering of both the panchromatic image and the intensity component of the original multispectral image. Ikonos and QuickBird data are used to assess the FFT-enhanced IHS transform method. Experimental results indicate that the FFT-enhanced IHS transform method may improve upon the standard IHS transform and the PCA methods in preserving spectral and spatial information.     

利用最优估计理论进行多光谱与全色影像融合

应用小波变换分解遥感影像,利用遥感影像自身的先验信息——空间分辨率确定高频域融合过程中的权值,使用最小二乘估计与小波重构完成影像融合。实验结果表明,相对于参考的其他融合方法,此方法在注入全色影像空间细节和保持多光谱影像的光谱信息方面性能更佳。     

DCT Based Pansharpening of Satellite Images Using Adaptive Linear Model

Remote sensing offers a wide variety of image data with different characteristics in terms of spatial and spectral resolutions. For optical sensor systems, imaging systems have a trade-off between high spatial and high spectral resolution, and no single system offers both. Hence, in the remote sensing application, an image with ‘greater quality’ often means higher spatial and higher spectral resolution. It is, therefore, necessary and very useful to merge images with higher spectral information and higher spatial information. Pansharpening combines spatial information from the high-resolution panchromatic image and color information from multispectral bands to create a high-resolution color image. Here we propose Discrete Cosine Transform (DCT) based pansharpening algorithm using Adaptive Linear model which preserves spectral information from Multispectral image and retains spatial resolution of Panchromatic image.     

基于小波变换的多光谱图像压缩方法

在分析多光谱图像小波变换后系数特点的基础上,提出了一种共享有效图的小波变换压缩方法（ＳＳＭＷＴ）。该方法将小波变换压缩技术中的零树编码推广到多光谱图像压缩中,利用多光谱图像的结构相关性,对多幅小波图像只需构造一幅有效图,同时去除空间冗余和谱间结构冗余,并与Ｋ－Ｌ变换相结合,进一步去除谱间统计冗余,实验表明了该方法的有效性。     

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