CBERS-02B星HR与CCD影像融合研究

CBERS-02B星搭载了高分辨率全色相机HR和多光谱传感器CCD,HR和CCD分别具有高空间分辨率和高光谱分辨率的特点。对这两类影像进行融合可以得到尽可能完整和准确的地物观测影像。对北京地区CBERS-02B的HR和CCD影像分别用HSV变换、HPF变换、Brovey变换、HIS变换、SFIM变换和小波变换等融合方法进行融合,对融合结果从空间细节和光谱保持两个方面分别定性和定量地进行评价,并比较这几种融合算法在CBERS-02B的HR和CCD遥感影像融合中的效果,得到了几种比较合适的融合方法。     

基于亮度平滑滤波调节(SFIM)的SPOT5影像融合

遥感影像融合不仅要求提高原多光谱影像的空间分辨率,更重要的是保留影像的光谱信息,减少失真。为分析SFIM算法融合SPOT-5影像的光谱保真效果,首先采用IHS、小波和SFIM算法对SPOT-5的全色影像和多光谱影像进行融合,生成新的高分辨率多光谱影像。然后采用均值、相关系数、熵、标准差、梯度五个指标对SFIM不同滤波窗口的融合结果评价;最后采用以上定量评价指标对不同方法的融合结果进行分析评价。结果表明:和传统的IHS、小波融合方法相比,SFIM融合不仅提高了影像的空间分辨率和清晰度,而且较好地保持了原多光谱影像的光谱特征。     

多光谱与高分辨率图像融合方法比较研究

通过分析比较HIS变换、Brovey变换、SFIM变换、加权融合四种常用融合方法的原理及其优缺点,提出了使融合后图像光谱扭曲和失真较小,同时保持高分辨率图像的空间纹理细节信息的融合方法应满足的三条规则。并依据此规则,提出了一种基于高通滤波算子的边缘增强型的融合算法,实验结果表明此算法具有很好的空间分辨率,同时又能较好地保持原有多光谱图像的光谱特性,验证了规则的合理性。     

基于多种变换的遥感图像新型融合方法

针对多光谱图像空间分辨率低这一特点,提出一种在PCA变换基础上,利用小波变换和高通滤波相结合的图像融合算法。实现了ETM+全色波段与ETM+多光谱波段图像的融合,并从空间纹理信息,光谱真实性两个方面进行定性和定量评价。研究表明,该融合算法产生的光谱失真较小,同时很大程度地保持了高分辨率全色波段的空间纹理细节信息,是一种较好的图像融合方法。     

基于遥感图像的颜色保真融合方法的研究*

针对遥感图像融合Brovey变换法存在颜色失真的现象,提出了一种低通比值融合法。该融合法首先对高几何分辨率的全色波段进行低通滤波,然后将低分辨率多光谱图像与全色波段图像相乘,再除以滤波后的全色波段图像,便得到融合图像。从辐照的角度证明了该低通比值融合法具备理论基础,并从目视评价、定量分析、分类精度证实了该低通比值融合法优于Brovey变换法,该低通比值融合法是一种能较好地保全低分辨率多光谱图像颜色的融合方法。     

用低通滤波器改进Brovey融合法*

针对遥感图像融合Brovey变换法存在颜色失真的现象,提出了一种低通比值融合法。该融合法首先对高几何分辨率的全色波段进行低通滤波,然后将低分辨率多光谱图像与全色波段图像相乘,再除以滤波后的全色波段图像,便得到融合图像。从辐照的角度证明了该低通比值融合法具备理论基础,并从目视评价、定量分析、分类精度证实了该低通比值融合法优于Brovey变换法。该低通比值融合法能较好地保全低分辨率多光谱图像颜色的融合方法。     

颜色保真的遥感图像融合方法的研究

针对遥感图像融合Brovey变换法存在颜色失真的现象,提出了一种低通比值融合法,该融合法首先对高几何分辨率的全色波段进行低能滤波,尔后将低分辨率多光谱图像与全色波段图像相乘,再除以滤波后的全色波段图像,便得到融合图像。从辐照的角度证明了该低通比值融合法具备理论基础,并从目视评价、定量分析、分类精度证实了该低通比值融合法优于Brovey变换法,该低通比值融合法是一种能较好地保全低分辨率多光谱图像颜色的融合方法。     

面向土地覆盖分类的MODIS影像融合研究

MODIS影像的多波段及其1、2波段的250 m中等分辨率为大区域中空间分辨率的土地覆盖制图提供了可能。为了有效利用MODIS影像的空间和光谱信息,使用SFIM、HPF和PCA变换等遥感影像融合方法,分别采用MODIS影像的波段1(b1)和波段2(b2)对3~7(b3~b7)波段进行融合,并就融合影像的光谱保真度和分类精度对6种不同融合结果进行评价。结果表明不同的融合结果得到的分类精度均有不同程度的提高;3种融合方法中使用b2的融合效果均优于b1;SFIM变换在光谱失真较小的情况下能够较大程度地提高分类精度。因此使用b2的SFIM变换可以用于提高MODIS土地覆盖图的空间分辨率和精度。     

一种基于SVR的简易融合算法

基于SVR (Synthetic Variable Ratio, 合成变量比) 融合方法,本文提出了一种SSVR (Simplified Synthetic Variable Ratio, 简易合成变量比) 融合方法.SSVR的核心是融合采用Radiance (辐射亮度) 而不是DN值,低分辨率多光谱模拟的全色波段能量用高分辨率全色波段的能量替代.以Landsat 7 ETM 数据为例,采用PCA (主成分分析) 、Multiplicative (乘积方法) 、Brovey Transform (比值方法) 、ISVR、SSVR 5种方法进行遥感影像融合,对比结果表明SSVR融合方法在提高原始多光谱影像分辨率的基础上,很好地保持了多光谱信息,该方法简单易行,提高了遥感数据的可利用性.     

CBERS-02与SPOT5融合数据及其在海岸带土地利用调查中应用能力综合评价

以珠海市横琴岛为试验区,对CBERS-02星多光谱数据和SPOT5全色数据进行融合处理,融合方法包括HIS变换法、Brovey法、主成分融合法、小波融合法、SFIM法、Gram\|Schmidt法、PANSHARP法,并使用定量指标对融合结果进行分析,通过计算各波段均值、信息熵、标准差、相关系数等指标对各融合结果进行分析评价,探讨适合两种数据的融合方法;对CBERS-02和SPOT5数据的PANSHARP法融合数据进行支持向量机分类,总体分类精度为82.55%,Kappa系数为0.7358;试验结果表明,通过融合CBERS\|02多光谱数据与SPOT5全色数据,既可以增强空间纹理信息,又可以弥补SPOT5多光谱数据缺少蓝光波段的缺陷,具有丰富的光谱信息,可以在海岸带土地利用调查广泛应用。     

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.     

地质灾害调查中ETM+与SPOT-5 Pan融合方法比较

ETM+与SPOT-5Pan融合影像含有丰富的光谱信息和空间结构信息,是地质灾害调查的理想数据源。但ETM+多光谱图像与SPOT-5 Pan图像空间分辨率相差悬殊,直接融合可能导致融合影像色彩模糊、色调不和谐等问题;若将ETM+与其全色融合后再与SPOT-5 Pan融合,如果融合方法不当亦会造成光谱信息的过多损失。通过采用IHS、Brovey、PCA和Gram-Schmidt变换分别对ETM+和SPOT-5 Pan按上述两种途径进行直接和间接融合,采用主观评定以及光谱剖面分析对融合影像进行了评价,从而筛选出适合于地质灾害调查的ETM+与SPOT-5 Pan融合方法。     

利用ASTER数据评价ETM+遥感数据自身融合效果

ETM+自身融合后的遥感影像折中了全色波段的高空间分辨率和多光谱波段的光谱分辨能力。通过同一季相和同一地区的ASTER影像和ETM+自身融合后影像的极限放大进行对比分析,发现ETM+自身融合后的彩色合成影像的空间分辨力达不到15 m,但能保证到17.3 m;通过ASTER影像和ETM+融合后影像的NDVI植被指数的对比分析,发现ETM+自身融合后的彩色合成影像的综合光谱分辨能力仍能保持ETM+彩色合成影像的73%以上。ETM+自身融合效果的好坏与融合算法优劣也具有一定的关系。     

小波局部高频替代融合方法

IHS变换是图象融合的经典算法之一。采取IHS变换与小波变换结合的融合算法是近年发展起来的遥感数据融合方法,发挥了IHS和小波两种变换算法的优点。效果比用单一一种方法更显著,提出了小波局部高频替代的融合方法。ETM遥感数据5、4、3波段由IHS盂塞尔彩色空间正变换后的Ⅰ亮度分量,经与全色波段中高能量替代的小波变换,形成一个新的集中了Ⅰ亮度分量和全色波段高频能量的新分量,再通过IHS进行反变换,经过实验,表明此方法可显著提高融合后图象的分辨率,同时又保持了原来多光谱图象的光谱信息。将此方法处理结果用在四川岷江“退耕还林”遥感调查项目中,可以提高土地利用计算精度。     

基于小波变换理论的影像融合及评价

研究了基于MALLAT小波变换与静态小波变换(SWT)改进的IHS影像融合方案,并与标准IHS,BROVEY,PCA融合算法进行对比,分析了各种融合结果的优缺点。采用信息熵,标准差指标对信息量进行比较。选择平均值,相关系数,标准偏差指数进行融合影像光谱特征评价。针对ETM 全色波段与多光谱波段进行实验分析,结果表明小波变换融合算法总体优于传统融合算法,并能较好的保持光谱特性,但两种不同的离散小波变换方法改进的融合效果各有优缺点。     

中高分辨率遥感影像融合研究

以浙江省衢州市的一个区域作为研究区,在阐述遥感图像融合原理的基础上,以QuickBird、SPOT5和Landsat ETM+全色与多光谱遥感图像为数据源,应用Brovey变换、SVR变换、PCA变换、Pansharp变换和Gram-schmidt变换等融合方法,对上述3类遥感图像进行了融合试验和分析,并从光谱信息和空间信息两个方面对融合效果进行计算分析,来比较这几种融合算法用于QuickBird、SPOT5、ETM+遥感图像融合的效果。研究结果表明,在QuickBird影像融合时Gram-schmidt变换法的效果最好。Pansharp变换法在SPOT5影像融合时综合效果优于其它方法。ETM+影像融合时,Gram-schmidt变换的光谱退化最小;在空间信息增强方面:Brovey变换在波段2、3融合时效果最好,在波段1、5、7上,SVR变换法优于其它变换法,而波段4融合时则是Pansharp变换最佳。     

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.     

Assessment of pan-sharpened very high-resolution WorldView-2 images

The fusion of multispectral (MS) images with high spatial resolution panchromatic (pan) images compensates the trade-off between spatial and spectral resolutions. The performance of fusion algorithms for different sensors is an active area of research. Therefore, with the availability of new very high-resolution (VHR) sensors, it becomes customary to evaluate the applicability of existing fusion techniques. In this study, we focused on the WorldView-2 (WV2) spaceborne sensor, which captures data in eight MS (2 m spatial resolution) bands and one pan (0.5 m spatial resolution) band. We compared and assessed 12 fusion techniques, namely Brovey transform (BT), Ehlers, Gram–Schimdt (GS), hyperspherical colour sphere (HCS), high-pass filter (HPF), modified intensity hue saturation (ModIHS), Multiplicative, PANSHARP, PANSHARP2, principal component (PC), and wavelet (IHS and PC)-based methods. To measure the quality of fused products, qualitative and quantitative methods were used. In qualitative methods, visual analysis of different colour composites was carried out. In quantitative methods, band-wise eight quality metrics including the required processing time and controlling parameters were reported. For overall image quality assessment, it is necessary to combine the values of different quality metrics. Therefore, a mean observation score based on these values was calculated to rank the fusion methods. It was observed that the HPF and PANSHARP methods produced the most visually appealing images, whereas quantitative values indicated that HCS, HPF, and PANSHARP methods performed better than other methods. Combining the results of quantitative and qualitative analyses, it was found that PANSHARP and HPF methods are superior to other methods in preserving both spatial and spectral details.