基于2DPCA-NSCT变换的多光谱与全色图像融合

鉴于应用单一主成分分析(PCA)或非下采样Contourlet(NSCT)变换进行多光谱和全色图像融合存在的问题,提出了一种2DPCA-NSCT变换图像融合算法.首先对多光谱图像各波段进行二维PCA变换,视其主成分为信号而少量非主成分为噪声予以忽略;然后对全色图像和第一主成分做NSCT分解,在频域对近似分量和多方向高频分量按不同的融合规则融合;最后通过NSCT反变换得到融合图像.实验结果表明,所提出的融合算法在保持PCA变换良好的空间分辨率的同时改善了其光谱失真的问题.     

基于PCA和NSCT变换的遥感图像融合方法

为了改善非下采样Contourlet变换(NSCT)在图像细节信息表达的缺失问题,提出了一种新的基于主成分分析(PCA)和NSCT的遥感图像融合方法。首先对低空间分辨率多光谱(MS)图像进行PCA变换,提取第一主分量(PC1);其次,对PC1和高空间分辨率全色(PAN)图像进行NSCT变换,对二者的低频系数采用小波变换的融合规则,高频系数采用基于区域标准差自适应加权的融合规则;最后,经过PCA逆变换和NSCT逆变换得到融合图像。仿真实验结果表明,该方法不仅有效地融合了源图像的细节信息,而且得到了较好的视觉效果和较优的评价指标。     

基于改进NSCT和IHS变换的遥感影像融合方法

目的 为了增强多光谱和全色影像融合质量,提出基于脉冲耦合神经网络（PCNN）的非下采样Contoulet变换（NSCT）和IHS变换相结合的融合方法。方法 先对多光谱图像进行IHS变换提取亮度I分量,采用主成分分析增强I分量得到新的I+分量;然后通过NSCT变换分别对I+分量和全色图像进行分解,并采用边缘梯度信息激励的PCNN得到融合图像的低频和高频分量;最后进行NSCT逆变换、IHS逆变换得到融合图像。结果 利用资源一号02C卫星数据进行实验,结果表明该算法在保留光谱信息的同时提高了图像空间分辨率,获得了较好的融合效果。结论 结合NSCT和IHS变换的融合方法在视觉效果和客观评价指标上都优于常用的图像融合方法。     

基于改进的IHS、PCA和小波变换的遥感图像融合算法

文章提出了基于改进的IHS、PCA和小波变换的遥感图像融合算法,提高融合图像的空间分辨率和光谱分辨率,首先对多光谱图像进行PCA变换,使其维度降低,减少信息损失,将原始图像数据中有效的主要信息用主成分PC1、PC2、PC3表示.接着对主成分进行IHS变换得到I、H、S分量,之后将强度分量I与全色图像进行直方图优化求解得到newPAN,最后对newPAN和强度分量I进行小波分解.利用PCA对多光谱图像操作后再进行IHS变换,弥补了传统IHS算法只能处理三个波段多光谱图像的缺陷,增加了处理的波段数,而且PCA融合算法的光谱保持度较高,该算法将IHS、PCA、小波变换三种融合算法相结合,利用各个算法的优势,最大程度地减少替换成分相关性不高造成的光谱扭曲,克服小波变换融合过程中产生的细节信息畸变问题.     

结合熵主成分变换与优化方法的遥感图像融合

在遥感图像融合中,融合图像光谱失真是主要存在的问题,为此提出一种结合熵主成分变换与优化方法的图像融合方法。通过熵主成分变换将庞杂的多波段数据用尽可能少的波段表示出来,减少光谱维数,且从熵的贡献角度出发完成投影变换保留更多的源波段信息。取第一熵主分量,与直方图匹配后的全色图像进行小波变换,分别获取低频和高频子图。对低频子图采用量子粒子群优化方法搜索线性加权的最优融合权值,对高频子图采用统计特征与统计模型相结合的方式完成融合,小波融合结果作为第一熵主分量。最后,熵主成分逆变换得到融合后的遥感图像。选用熵、交叉熵、标准差、梯度、相关系数和光谱扭曲度作为客观评价指标。实验结果表明,所提方法能够提升空间细节且避免融合图像光谱失真。     

TM+SPOT图像的二次融合方法

提出了一种基于PCA变换和IHS变换的小波二次融合方法。首先将多光谱图像和高空间分辨率图像进行ISH变换融合得到第一次融合图像;再将此图像和原多光谱图像进行PCA变换,分别对第一主成分进行小波多尺度分解至适当层次并进行方向对比度融合,最后作PCA逆变换得到最终的融合图像。实验结果表明,文中方法在保留光谱信息的同时提高了空间分辨率,其客观性能指标均优于其它方法。     

一种改进的遥感图像融合方法:LFF

通过遥感图像融合,可获得更丰富的信息,常用遥感图像融合方法如HIS彩色变换可将不同平台、不同光谱响应范围的高空间分辨率的遥感数据与多光谱遥感数据进行融合,但要求这两组数据的光谱响应范围一致,否则便会产生光谱扭曲的现象,从而影响了地物的识别。针对不同平台、不同光谱响应范围的遥感数据,该文在HSI变换的基础上,提出了一种改进的方法,即LFF融合法,首先对高几何分辨率的全色波段进行LoG滤波,而后将LoG滤波后的全色波段与多光谱经HSI正变换后的强度分量进行灰度直方图匹配,并替换之,经HSI逆变换便得到融合图像。论文从灰度变化指数和分类精度两方面分析了光谱保持性能和融合图像的分类精度,分析结果表明:LFF融合法的光谱保持性能优于HSI变换法,LFF融合后图像的分类精度高于HSI融合后的图像,LFF融合法是一种能较好地保持光谱特性的融合方法。     

基于Curvelet和2DPCA的遥感图像融合算法

根据图像处理不同算法模型的特点,提出了一种基于Curvelet和2DPCA变换相结合的遥感图像融合算法。首先,对多光谱图像进行2DPCA变换,获得其最佳投影轴集合U及特征向量矩阵Q,按照投影规则将多光谱图像投影到U上,得到各主成分分量Yk;再将与多光谱图像进行过直方图匹配的高分辨率图像投影到Q上,获得其主成分PanM及其它主成分分量,将PanM与Yk分别进行Curvelet变换,得到对应的高、低频系数;然后,根据相应的融合规则,对处理后的系数进行Curvelet逆变换,得到融合子图像;最后,将高分辨率图像的其他主成分分量与融合子图像进行2DPCA逆变换得到融合后图像。应用多光谱图像和高分辨率图像进行了融合实验,并将实验结果与其他方法进行比较。实验结果表明,该方法能够在保持源数据光谱特性的同时,较好的提高空间分辨率。     

非下采样方向滤波器组在遥感图像融合中的应用

利用具有平移不变性的非下采样方向滤波器组(NSDFB),结合具有平移不变性的àtrous小波变换,提出了一种基于NSDFB的低分辨率多光谱图像和高分辨率全色图像的融合方法.将多光谱图像亮度、色度、饱和度(IHS)色彩空间的I分量和全色图像(PAN)分别进行àttrous小波变换,并对得到的高频系数分别进行NSDFB分解,从而得到多方向的高频信息,然后将高低频系数分别通过一定的融合算子进行融合、重构,得到I'分量,最后通过IHS逆变换得到融合图像.实验结果表明,在几种不同的客观评价标准下,该方法优于传统的IHS、主成分分析法(PCA)和小波变换方法的融合效果,能有效地改善主成分分析法(PCA)和小波变换所带来的虚假边缘和光谱扭曲现象.     

基于Iαβ色彩空间和Contourlet变换相结合的融合方法*

针对北京1号小卫星的多光谱与全色波段的分辨率比率较大,传统的融合方法会产生边界模糊和光谱扭曲现象,提出了一种新的融合算法。首先对多光谱与全色影像分别进行Iαβ和Contourlet变换;然后在频率域中采用不同的融合策略进行处理;最后进行Contourlet和Iαβ逆变换,得到融合图像。实验表明,本方法既提高了融合图像的空间细节信息又很好地保持了图像的光谱特征,优于传统的融合方法。     

Pansharpening using data-centric optimization approach

Earth’s observation satellites provide simultaneously both multispectral (XS) and panchromatic (pan) images but XS image has a lower spatial resolution when compared to pan image. Pansharpening is a pixel-level fusion technique resulting in a high-resolution multispectral image in terms of both spatial and spectral resolution. The problem lies in maintaining the spectral characteristics of each channel of the XS image when pan image is used to estimate the high spatial XS image. Many techniques have been proposed to address the problem. A popular method involves a sensor-based approach where correlation among the XS channels and correlation between the pan and spectral channels are incorporated. In this paper, we take a wholesome approach based on the reflectance data irrespective of the sensor physics. A linear regression model is formulated between the XS channel and the panchromatic data. We formulate an optimization problem in terms of Lagrange multiplier to maximise the spectral consistency of the fused data with respect to the original XS data, and to minimise the error in variance between the reference data and the computed data. We validate and compare our method with IHS and Brovey methods based on evaluation metrics such as Chi-square test and the R² test. The implementation is done and presented using IKONOS satellite data.     

The effect of fusing Sentinel-2 bands on land-cover classification

The Sentinel-2 satellite currently provides freely available multispectral bands at relatively high spatial resolution but does not acquire the panchromatic band. To improve the resolution of 20 m bands to 10 m, existing pansharpening methods (Brovey transform [BT], intensity–hue–saturation [IHS], principal component analysis [PCA], the variational method [P + XS], and the wavelet method) required adjustment, which was achieved using higher resolution multispectral bands in the role of a panchromatic band to fuse bands at a lower spatial resolution. After preprocessing, six bands at lower resolution were divided into two groups because some image fusion methods (e.g. BT, IHS) are limited to a maximum of three input bands of a lower resolution at a time. With respect to the spectral range, the higher resolution band for the first group was synthesized from bands 4 and 8, and band 8 was selected for the second group. Given that one of the main remote sensing applications is land-cover classification, the classification accuracy of the fusion methods was assessed as well as the comparison with reference bands and pixels. The supervised classification methods were Maximum Likelihood Classifier, artificial neural networks, and object-based image analysis. The classification scheme contained five classes: water, built-up, bare soil, low vegetation, and forest. The results showed that most of the fusion methods, particularly P + XS and PCA, improved the overall classification accuracy, especially for the classes of forest, low vegetation, and bare soil and in the detection of coastlines. The least satisfying results were obtained from the wavelet method.     

一种基于残差的遥感图像融合新方法

针对遥感图像融合问题,提出了一种基于残差的遥感图像融合新方法。该方法借助于主成分分析(principal component analysis,PCA),通过对多光谱图像的残差图像和全色图像的残差图像进行融合来恢复出多光谱图像的高分辨率残差图像,以实现多光谱图像和全色图像的融合。实验结果的主观视觉效果和客观统计参数分析都表明,新方法不仅较大地增强了融合图像的空间细节表现能力,而且很好地保留了多光谱图像的光谱信息,其性能优于现有的HIS(hue-intensity-saturation)变换融合方法、PCA融合方法和小波变换(wavelet transform,WT)融合方法。     

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IHS变换与小波变换的遥感图像融合

针对多光谱图像与全色图像的融合，本文提出了一种基于IHS变换和小波变换的遥感图像融合方法。新方法首先对多光谱图像作IHS变换，得到亮度I，色度H，饱和度S三个分量；其次，利用小波变换融合方法融合多光谱图像的亮度分量与全色图像，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。主观视觉效果分析和客观统计参数评价分析表明，新方法的性能优于IHS变换融合方法、小波变换融合方法和PCA变换融合方法，不仅较大地增强了融合图像的空间细节表现能力，而且很好地保留了多光谱图像的光谱信息。     

Remote sensing image fusion based on Bayesian linear estimation

A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation (BLE) is applied to observation models between remote sensing images with different spa- tial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral (MS) images, instead of assuming the joint distribution between the panchromatic (PAN) image and low-resolution multispectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis (PCA) method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation, PCA-based method and wavelet-based method.     

IKONOS image fusion process using steepest descent method with bi-linear interpolation

There are many image fusion processes to produce a high-resolution multispectral (MS) image from low-resolution MS and high-resolution panchromatic (PAN) images. But the most significant problems are colour distortion and fusion quality. Previously, we reported a fusion process that produced a1 m resolution IKONOS fused image with minimal spectral distortion. However, block distortion appeared at the edge of the curved sections of the fused image, which was reduced by performing the wavelet transformation as a post-process. Here, we propose an image fusion process using the steepest descent method with bi-linear interpolation, which can remove block distortion without using wavelet transformation. Bi-linear interpolation provides the proper initial values of the fused image, and then the steepest descent method produces the optimum results of the fusion process. These results achieve improvement on the spectral as well as spatial quality of a1 m resolution fused image when compared with other existing methods and remove block distortion completely.     

TM and IRS-1C-PAN data fusion using multiresolution decomposition methods based on the 'a tròus' algorithm

Image fusion represents an important tool for remote sensing data elaborations. This technique is used for many purposes. Very often it is used to produce improved spatial resolution. The most common situation is represented by a pair of images: the first acquired by a multispectral sensor with a pixel size greater than the pixel size of the second image given by a panchromatic sensor (PAN). Starting from these images fusion produces a new multispectral image with a spatial resolution equal, or close, to that of the PAN. Very often fusion introduces important distortions on the pixel spectra. This fact could compromise the extraction of information from the image, especially when using an automatic algorithm based on spectral signature such as in the case of image classification. In this work we present the analysis of two fusion methods based on multiresolution decomposition obtained using the 'a tròus' algorithm and applied to a pair of images acquired by Thematic Mapper (TM) and Indian Remote Sensing (IRS)-1C-PAN sensors. The methods studied are also compared with two classical fusion methods, the intensity, hue and saturation (IHS) and standardized principal components (SPC). Fused results are studied and compared using various tests including supervised classification. Most of the tests used have been extracted from literature regarding the assessment of spatial and spectral quality of fused images. This study shows that the methods based on multiresolution decomposition outperform the classical fusion methods considered with respect to spectral content preservation. Moreover, it is shown that some of the quality tests are more significant than others. The discussion of this last aspect furnishes important indications for data quality assessment methods.     

基于CBERS-02B和SPOT-5全色波段的图像融合纹理信息评价研究

CBERS-02B是我国第一代传输型陆地资源遥感卫星,搭载的传感器可以获得2.36 m分辨率的全色波段数据。通过遥感影像融合技术,将CBERS-02B全色数据和SPOT-5全色数据与SPOT-5多光谱数据10 m分辨率的图像进行了多方法的融合处理,通过对融合后图像的空间纹理信息进行比较和评价,获得了纹理信息的特征参数值。通过目视评价和定量分析,认为用CBERS-02B全色数据融合的影像在空间纹理上比SOPT-5融合的影像有优势。因此,CBERS-02B的全色波段是一种较高质量的高分辨率数据,应用前景广阔。     

一种基于小波包变换的遥感影像融合方法

针对多光谱遥感影像和全色遥感影像,提出了一种基于小波包变换的遥感影像融合方法。新方法首先对多光谱遥感影像进行PCA变换;其次对多光谱遥感影像的第一主分量和全色遥感影像进行小波包变换;然后保留多光谱影像第一主分量的低频近似分量,融合它们的高频细节分量;最后,做小波包反变换,得到新的多光谱遥感影像第一主分量,再做PCA反变换,得到新的多光谱遥感影像。与PCA变换融合方法、IHS变换融合方法和小波变换融合方法等方法在主观视觉效果分析和客观统计参数两方面做了比较,新方法是有效的,不仅较大地增强了结果影像的空间细节表现能力,而且很好地保留了多光谱影像的光谱信息。