运用主成分变换-逆变换对ADEOS AVNIRXS和PAN进行复合的研究

信息复合的目的是为了把多种遥感器数据的特性进行互补。复合图像与原图像相比,既具有多光谱特性,而且空间分辨率也得到了提高,这样它更有利于目视判读或计算机自动化处理。运用主成分变换-逆变换的方法对ADEOSAVNIR的多光谱数据(16 m)和全色波段数据(8 m)进行了复合研究。即先对多光谱图像进行主成分变换,求出第一主成分。然后用全色波段数据取代第一主成分并进行主成分逆变换得到所需的复合图像。复合图像的空间分辨率为8 m,而且具有4个波段。     

A Variational Model for P+XS Image Fusion

We propose an algorithm to increase the resolution of multispectral satellite images knowing the panchromatic image at high resolution and the spectral channels at lower resolution. Our algorithm is based on the assumption that, to a large extent, the geometry of the spectral channels is contained in the topographic map of its panchromatic image. This assumption, together with the relation of the panchromatic image to the spectral channels, and the expression of the low-resolution pixel in terms of the high-resolution pixels given by some convolution kernel followed by subsampling, constitute the elements for constructing an energy functional (with several variants) whose minima will give the reconstructed spectral images at higher resolution. We discuss the validity of the above approach and describe our numerical procedure. Finally, some experiments on a set of multispectral satellite images are displayed.     

Spatially adaptive multi-resolution multispectral image fusion

Due to the performance limit of remote sensing systems, multispectral images have limited spatial resolution. Their spatial resolution can be improved by merging them with higher resolution image data. A fundamental problem frequently occurring in existing fusion processes, however, is the distortion of spectral information. This paper presents a spatially adaptive image fusion algorithm which produces visually natural images and retains the quality of local spectral information as well. High frequency information of the high resolution image to be inserted to the resampled multispectral images is controlled by adaptive gains to incorporate the difference of local spectral characteristics between the high and the low resolution images into the fusion. Each gain is estimated to minimize the l ₂-norm of the error between the original and the estimated pixel values defined in a spatially adaptive window of which the weights are proportional to the spectral correlation measurements of the corresponding regions. This method is applied to a set of co-registered Landsat 7 Enhanced Thematic Mapper (ETM)+ panchromatic and multispectral image data. The experimental results show that high resolution images can be synthesized by the proposed method, which successfully preserves spectral content of the multispectral images.     

一种新的全色与多光谱图像融合变分模型

图像融合是提供包含各输入图像互补信息的单幅图像的有力工具. 本文提出了一种新的用于全色和多光谱图像融合的变分模型. 在Socolinsky对比度模型的基础上构造了一个改进的能量泛函最小化问题, 以寻找最接近全色图像梯度的解.为了提高多光谱图像的空间分辨率,并尽可能地保持其原有的光谱信息, 还将光谱一致项、波段间相关项和对比度增强项引入融合模型. 在IKONOS和QuickBird数据集上测试了该模型的性能.实验结果表明该模型可以生成同时具有高空间质量和高光谱质量的融合图像.     

The joint use of IHS transform and redundant wavelet decomposition for fusing multispectral and panchromatic images

Intensity hue saturation (IHS) and wavelet decomposition are two distinct fusion methods used for enhancing the spatial resolution of multispectral images by exploiting a high-resolution panchromatic image. In this paper, a combination of the IHS transform and redundant wavelet decomposition is proposed as a general method for fusing multisensor images. The principle consists of transforming low-resolution multispectral images into IHS independent components. The low-resolution intensity component is fused with the high-resolution panchromatic image in the redundant wavelet domain through an appropriate model. Subsequently, the high-resolution intensity produced is substituted to the low-resolution intensity. High spatial resolution multispectral images are then obtained through an inverse IHS transformation. SPOT images are used to illustrate the superiority of this approach over the IHS fuser in terms of preservation of spectral properties.     

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.     

Pan-sharpening:a fast variational fusion approach

A fast variational fusion model based on partial differential equations (PDEs) is presented for pansharpening. The functional framework consists of several energy terms. The gradient energy term is created by calculating the gradient vector field of the panchromatic image and the geometry of the pan is injected into the multi-spectral bands. The radiometric reduction energy term and the channel correlation energy term are defined to decrease the radiometric distortion and preserve the correlation of multi-spectral channels while enforcing the inter-bands coherence. Inspired by the shock-filtering model, an inverse diffusion term for image enhancement is put to PDEs which are deduced by minimizing the energy functional. In comparison with the state-of-the-art fusion approaches based on `a trous wavelet and non-sampled contourlet, our model can obtain the fused image with a high spatial or spectral quality by adjusting the weight coefficients of the energy terms. It can also achieve a rather good trade-off between the spatial resolution improvement and the spectral quality preserving. Our model’s computational complexity for one time step is only O(N).     

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。     

自适应权重注入机制遥感图像融合

目的 遥感图像融合是将一幅高空间分辨率的全色图像和对应场景的低空间分辨率的多光谱图像,融合成一幅在光谱和空间两方面都具有高分辨率的多光谱图像。为了使融合结果在保持较高空间分辨率的同时减轻光谱失真现象,提出了自适应的权重注入机制,并针对上采样图像降质使先验信息变得不精确的问题,提出了通道梯度约束和光谱关系校正约束。方法 使用变分法处理遥感图像融合问题。考虑传感器的物理特性,使用自适应的权重注入机制向多光谱图像各波段注入不同的空间信息,以处理多光谱图像波段间的差异,避免向多光谱图像中注入过多的空间信息导致光谱失真。考虑到上采样的图像是降质的,采用局部光谱一致性约束和通道梯度约束作为先验信息的约束,基于图像退化模型,使用光谱关系校正约束更精确地保持融合结果的波段间关系。结果 在Geoeye和Pleiades卫星数据上同6种表现优异的算法进行对比实验,本文提出的模型在2个卫星数据上除了相关系数CC（correlation coefficient）和光谱角映射SAM（spectral angle mapper）评价指标表现不够稳定,偶尔为次优值外,在相对全局误差ERGAS（erreur relative globale adimensionnelle de synthèse）、峰值信噪比PSNR（peak signal-to-noise ratio）、相对平均光谱误差RASE（relative average spectral error）、均方根误差RMSE（root mean squared error）、光谱信息散度SID（spectral information divergence）等评价指标上均为最优值。结论 本文模型与对比算法相比,在空间分辨率提升和光谱保持方面都取得了良好效果。     

Image Fusion Based on NSCT and Sparse Representation for Remote Sensing Data

The practice of integrating images from two or more sensors collected from the same area or object is known as image fusion. The goal is to extract more spatial and spectral information from the resulting fused image than from the component images. The images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral images. This study provides a novel picture fusion technique that employs L0 smoothening Filter, Non-subsampled Contour let Transform (NSCT) and Sparse Representation (SR) followed by the Max absolute rule (MAR). The fusion approach is as follows: first, the multispectral and panchromatic images are divided into lower and higher frequency components using the L0 smoothing filter. Then comes the fusion process, which uses an approach that combines NSCT and SR to fuse low frequency components. Similarly, the Max-absolute fusion rule is used to merge high frequency components. Finally, the final image is obtained through the disintegration of fused low and high frequency data. In terms of correlation coefficient, Entropy, spatial frequency, and fusion mutual information, our method outperforms other methods in terms of image quality enhancement and visual evaluation.     

Effects of spatial resolution ratio in image fusion

In image fusion, the spatial resolution ratio can be defined as the ratio between the spatial resolution of the high‐resolution panchromatic image and that of the low‐resolution multispectral image. This paper attempts to assess the effects of the spatial resolution ratio of the input images on the quality of the fused image. Experimental results indicate that a spatial resolution ratio of 1 : 10 or higher is desired for optimal multisensor image fusion provided the input panchromatic image is not downsampled to a coarser resolution. Due to the synthetic pixels generated from resampling, the quality of the fused image decreases as the spatial resolution ratio decreases (e.g. from 1 : 10 to 1 : 30). However, even with a spatial resolution ratio as small as 1 : 30, the quality of the fused image is still better than the original multispectral image alone for feature interpretation. In cases where the spatial resolution ratio is too small (e.g. 1 : 30), to obtain better spectral integrity of the fused image, one may downsample the input high‐resolution panchromatic image to a slightly lower resolution before fusing it with the multispectral image.     

一种基于HT系数预测的遥感图像多分辨融合方法

信息融合的概念自70年代形成后,由于其强调应用对象结果的信息优化,在许多方面比传统的信息处理方法有更大的优势,在遥感领域得到了广泛的应用。本文在分析前人算法的基础上,揭示了高斯拉普拉斯金字塔（GLP）结构融合方法与传统的哈达玛变换（HT）之间的内在联系,提出了一种基于HT系数预测的多分辨融合方法。通过实验表明,这一算法对于光谱差异较大的遥感图像的融合,在几何分辨率改善的同时可获得较好的光谱保真度。     

Multispectral image adaptive pansharpening based on wavelet transformation and NMDB approaches

In remote sensing, satellite images acquired from sensors provide either high spectral or high spatial resolution. The pansharpening framework is applied to remote-sensing systems to enhance the spatial quality of coarse-resolution multispectral (MS) images using information from panchromatic imagery. A multidecomposition pansharpening approach combining MS and panchromatic (PAN) images is proposed in this paper in order to bring the resolution of the low-resolution MS imagery up to that of the panchromatic images. In particular, multilevel wavelet decomposition is applied to the luminance-chrominance (YUV) space transformation (taking into account the red green and blue (RGB) bands) or extended-YUV transformation (taking into account the near infrared (NIR) band in addition to RGB) of the original MS channels, where geometrical details from the panchromatic image are introduced into the MS ones. Our approach contains a preprocessing step that consists of homogenizing the luminance, Y, and the panchromatic image reflectance, which are, respectively, a value integrated over a wavelength spectrum and simply a linear combination of some values in the same spectrum. Hence, as the panchromatic image reflectance and luminance reflectance correspond to different measurements, they do not correspond to the same physical information, which results in a difference between their histograms. Therefore, simple histogram matching is traditionally applied to panchromatic data to fit it to the luminance to avoid colour distortion after fusion. However, as the transformation concerns just the details of the panchromatic and MS images, a new scheme for matching the images which ignores the divergence between their approximations and maximizes the resemblance between their details is proposed in this work. After that, the fusion approach is applied, and in contrast to the original approach where the details of the fused MS luminance are set equal to the PAN luminance, we propose an adaptive approach in which just a part of the PAN details proportional to the similarity between the luminance and lowered PAN image is taken. Indeed, high-resolution geometrical details cannot be similar if the low-resolution details are not in good agreement. Besides, as the agreement between PAN and MS images depends on the occupation class, we have created a segmentation map and then computed separately the correlation in each region. Finally, the evaluation is done based on QuickBird and Pleiades-1A data sets showing rural and suburban areas. When compared to recent methods, our approach provides better results.     

基于l αβ空间的多光谱和全色图像融合

提出了一种新的基于lαβ空间的图像融合方法，该方法可以用来对低分辨率的多光谱图像和高分辨率的全色图像进行融合。该方法通过对多光谱图像和全色图像的融合，得到一幅融合后的图像，该融合后的图像集合了多光谱图像的光谱信息和全色图像的空间信息。实验结果表明该方法效果良好，优于传统的以及改进的IHS方法和PCA方法。     

Bidimensional Empirical Mode Decomposition for the fusion of multispectral and panchromatic images

Currently available image fusion techniques applied to the merging of fine resolution panchromatic and multispectral images are still not able to minimize colour distortion and maximize spatial detail. In this study, a new fusion method, based on Bidimensional Empirical Mode Decomposition (BEMD), is proposed. Unlike other multiresolution analysis tools, such as the discrete wavelet transform (DWT), which normally examines only horizontal, vertical and diagonal orthonormal details at each decomposed scale, the BEMD produces a fully two‐ dimensional decomposition of the panchromatic and multispectral images, based purely on spatial relationships between the extrema of the image. These are decomposed into a certain level of Intrinsic Mode Functions (IMFs) and residual images with the same number of columns and rows as the original image. In consequence, by injecting all the IMF images from the panchromatic image into the residue of the corresponding multispectral image, the fusion image may be reconstructed. The fusion results are evaluated and compared with other popular methods in terms of both the visual examination and the quantitative assessment of the merged images. Preliminary results show that BEMD is optimal and provides a delicate balance between spectral information preservation and enhancement of spatial detail.     

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.     

Enhancing multispectral imagery spatial resolution using optimized adaptive PCA and high-pass modulation

ABSTRACT

The pan-sharpening scheme combines high-resolution panchromatic imagery (HRPI) data and low-resolution multispectral imagery (LRMI) data to get a single merged high-resolution multispectral image (HRMI). The pan-sharpened image has extensive information that will promote the efficiency of image analysis methods. Pan-sharpening technique is considered as a pixel-level fusion scheme utilized for enhancing LRMI using HRPI while keeping LRMI spectral information. In this article, an efficient optimized integrated adaptive principal component analysis (APCA) and high-pass modulation (HPM) pan-sharpening method is proposed to get excellent spatial resolution within fused image with minimal spectral distortion. The proposed method is adjusted with multi-objective optimizationto determine the optimal window size and σfor the Gaussian low-pass filter (GLPF) and gain factor utilized for adding the high-pass details extracted from the HRPI to the LRMI principlecomponent of maximum correlation. Optimization results show that if the spatial resolution ratio of HRPI to LRMI is 0.50, then a GLPF of 5 × 5 window size and σ = 1.640 yields HRMI with low spectral distortion and high spatial quality. If the HRPI/LRMI spatial resolution ratio is 0.25, then a GLPF of 7 × 7 window size and σ = 1.686 yields HRMI with low spectral distortion and high spatial quality. Simulation tests demonstrated that the proposed optimized APCA–HPM fusion scheme gives adjustment between spectral quality and spatial quality and has small computational and memory complexity.     

多光谱图像与全色图像的像素级融合研究

以高空间分辨率的全色图像与高光谱分辨率的多光谱图像进行像素级双源融合为例，详细地总结了卫星多源遥感图像融合领域像素级融合的步骤、基本融合模型和优缺点，重点分析了各种常见像素级融合方法的原理和特点，并归纳了像素级融合结果的主客观评价标准和评价方法，以及像素级融合的主要应用领域，最后讨论了像素级融合目前存在的问题和今后的发展方向。     

Spectral change directions of multispectral subpixels in image fusion

In an image fusion process, the spatial detail in a panchromatic pixel is injected into the corresponding n-band multispectral (MS) subpixel to yield a synthetic pixel. The synthetic pixel can be regarded as the sum of three terms: the MS subpixel, a shift term, and a product of the spatial detail and an n-dimensional (n-D) spectral change vector. In this paper, the spectral change vector directions in some current image fusion methods are characterized and classified. As the spectral development of subpixels with the improvements in spatial resolution in imaging is analogous to the spectral change of MS subpixels with the improvements in spatial resolution in image fusion, the former is used as a reference to examine the spectral change vector directions in the current image fusion methods. Moreover, image haze and unmixing of mixed MS subpixels are highlighted as two aspects needing attention for further improvement in fusion quality.     

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.