Remote sensing satellite sensors feature a tradeoff between the spatial, temporal, and spectral resolutions. In this paper, we propose an integrated framework for the spatio-temporal-spectral fusion of remote sensing images. There are two main advantages of the proposed integrated fusion framework: it can accomplish different kinds of fusion tasks, such as multiview spatial fusion, spatio-spectral fusion, and spatio-temporal fusion, based on a single unified model, and it can achieve the integrated fusion of multisource observations to obtain high spatio-temporal-spectral resolution images, without limitations on the number of remote sensing sensors. The proposed integrated fusion framework was comprehensively tested and verified in a variety of image fusion experiments. In the experiments, a number of different remote sensing satellites were utilized, including IKONOS, the Enhanced Thematic Mapper Plus (ETM+), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Hyperspectral Digital Imagery Collection Experiment (HYDICE), and Syste me Pour l' Observation de la Terre-5 (SPOT-5). The experimental results confirm the effectiveness of the proposed method.

In this paper state-of-the-art and advanced methods for multispectral pansharpening are reviewed and evaluated on two very high resolution datasets acquired by IKONOS-2 (four bands) and WorldView-2 (eight bands). The experimental analysis allows us to highlight the performances of the two main pansharpening approaches (i.e. component substitution and multiresolution analysis).

The merging of multisensor image data is becoming a widely used procedure because of the complementary nature of various data sets. Ideally, the method used to merge data sets with high-spatial and high-spectral resolution should not distort the spectral characteristics of the high-spectral resolution data. This paper compares the results of three different methods used to merge the information contents of the Landsat Thermatic Mapper (TM) and Satellite Pour l'Observation de la Terre (SPOT) panchromatic data. The comparison is based on spectral characteristics and is made using statistical, visual, and graphical analyses of the results

State-of-the-art pansharpening methods generally inject the spatial structures of a high spatial resolution (HR) panchromatic (PAN) image into the corresponding low spatial resolution (LR) multispectral (MS) image by an injection model. In this paper, a novel pansharpening method with an edge-preserving guided filter based on three-layer decomposition is proposed. In the proposed method, the PAN image is decomposed into three layers: A strong edge layer, a detail layer, and a low-frequency layer. The edge layer and detail layer are then injected into the MS image by a proportional injection model. In addition, two new quantitative evaluation indices, including the modified correlation coefficient (MCC) and the modified universal image quality index (MUIQI) are developed. The proposed method was tested and verified by IKONOS, QuickBird, and Gaofen (GF)-1 satellite images, and it was compared with several of state-of-the-art pansharpening methods from both qualitative and quantitative aspects. The experimental results confirm the superiority of the proposed method.

In various applications of remote sensing, when high spatial resolution is required in addition with classification results, sensor fusion is a solution. From a set of images with different spatial and spectral resolutions, the aim is to synthesize images with the highest spatial resolution available in the set and with an appropriate spectral content. Several sensor fusion methods exist; most of them improve the spatial resolution but with a poor quality of the spectral content of the resulting image. Based on a multiresolution modeling of the information, the ARSIS concept (from its French name "Am lioration de la R solution Spatiale par Injection de Structures") was designed in the aim of improving the spatial resolution together with a high-quality in the spectral content of the synthesized images. The general case of application of this concept is described. A quantitative comparison of all presented methods is achieved for a SPOT image. Another example of the fusion of SPOT XS (20 m) and KVR-1000 (2 m) images is given. Practical information for the implementation of the wavelet transform, the multiresolution analysis, and the ARSIS concept by practitioners is given with particular relevance to SPOT and Landsat imagery.

This work presents a multiresolution framework for merging a multispectral image having an arbitrary number of bands with a higher-resolution panchromatic observation. The fusion method relies on the generalized Laplacian pyramid (GLP), which is a multiscale, oversampled structure. The goal is to selectively perform injection of spatial frequencies from an image to another with the constraint of thoroughly retaining the spectral information of the coarser data. The novel idea is that a model of the modulation transfer functions (MTF) of the multispectral scanner is exploited to design the GLP reduction filter. Thus, the interband structure model (IBSM), which is calculated at the coarser scale, where both MS and PAN data are available, can be extended to the finer scale, without the drawback of the poor enhancement occurring when MTFs are assumed to be ideal filters. Experiments carried out on QuickBird data demonstrate that a superior spatial enhancement, besides the spectral quality typical of injection methods, is achieved by means of the MTF-adjusted fusion.

<p>为了充分利用多源遥感图像的影像信息,针对不同分辨率的遥感图像进行融合算法研究。通过对基于小波变换(warelet transform,WT)与IHS变换的改进算法研究,提出了基于轮廓波变换(Contourlet transform,CT)与IHS变换的改进算法: 结合传统IHS彩色空间变换,将经IHS变换获得的多光谱图像亮度分量与原全色图像分别进行CT; 然后对得到的低频分量采用自适应融合规则、高频分量采用基于区域相似度的阈值控制规则分别进行融合; 最后对融合后的高频和低频分量进行Contourlet逆变换,得到最终的融合图像。对比实验结果表明: 本文提出的方法能够在有效保留光谱信息的同时,纳入全色图像丰富的空间细节信息。融合之后的结果图像与原多光谱图像具有更高的相关系数和更小的光谱畸变度,并且信息熵和标准差较传统WT及CT更优,具有一定的实用性。</p>

The intensity-hue-saturation (IHS) method, principal component analysis (PCA), Brovey transform (BT) and wavelet transform (WT) are the contemporary image fusion methods in remote sensing community. However, they often face color distortion problems with fused images. In other words, they are sensitive to the characteristics of the analyzed area. To investigate this color distortion problem, this work presents a relatively detailed study indicating that the color distortion problem arises from the change of the saturation during the fusion process. Meanwhile, PCA, BT, and WT are evaluated and found to be IHS-like image merging techniques. Experimental results for distinct image fusion methods are also demonstrated in this paper.

多光谱/全色影像融合可以得到高空间分辨率的多光谱影像, 在影像解译和分类等方面具有十分重要的意义。 提出一种基于梯度一致性约束的遥感影像融合方法。 该方法在最大后验概率框架下, 通过梯度一致性约束建立理想高空间分辨率多光谱影像和全色影像之间的关系, 并结合多光谱影像观测模型和Huber-Markov影像先验, 构建融合目标函数, 最后采用梯度下降法求解得到融合影像。 本文方法在目标函数中引入了梯度一致性约束, 克服了现有的同类方法受限于波段数量的缺陷, 同时在求解中自适应确定每个波段的迭代步长, 充分顾及了各波段的光谱特性, 从而既确保了融合影像的光谱信息保真度, 也提高了融合影像的空间信息融入度。 通过IKONOS和WorldView-2影像对该方法进行了验证, 并和GS, AIHS和AMBF等融合方法从定性和定量两方面进行了比较分析。 实验结果表明, 相比于其他方法, 该方法可以在更好保持光谱信息的同时增强影像的空间分辨率, 具有更广泛的适用范围和更佳的融合效果。

In this paper, an adjustable model-based image fusion method for multispectral (MS) and panchromatic (PAN) images is developed. The relationships of the desired high spatial resolution (HR) MS images to the observed low-spatial-resolution MS images and HR PAN image are formulated with image observation models. The maximum a posteriori framework is employed to describe the inverse problem of image fusion. By choosing particular probability density functions, the fused HR MS images are solved using a gradient descent algorithm. In particular, two functions are defined to adaptively determine most regularization parameters using the partially fused results at each iteration, retaining one parameter to adjust the tradeoff between the enhancement of spatial information and the maintenance of spectral information. The proposed method has been tested using QuickBird and IKONOS images and compared to several known fusion methods using quantitative evaluation indices. The experimental results verify the efficacy of this method.

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.

In this letter, we present a new method for the pansharpening of multispectral satellite imagery. Pansharpening is the process of synthesizing a high spatial resolution multispectral image from a low spatial resolution multispectral image and a high-resolution panchromatic (PAN) image. The method uses total variation to regularize an ill-posed problem dictated by a widely used explicit image formation model. This model is based on the assumptions that a linear combination of the bands of the pansharpened image gives the PAN image and that a decimation of the pansharpened image gives the original multispectral image. Experimental results are based on two real datasets and the quantitative quality of the pansharpened images is evaluated using a number of spatial and spectral metrics, some of which have been recently proposed and do not need a reference image. The proposed method compares favorably to other well-known methods for pansharpening and produces images of excellent spatial and spectral quality.

Super resolution image reconstruction allows the recovery of a high-resolution (HR) image from several low-resolution images that are noisy, blurred, and down sampled. In this paper, we present a joint formulation for a complex super-resolution problem in which the scenes contain multiple independently moving objects. This formulation is built upon the maximum a posteriori (MAP) framework, which judiciously combines motion estimation, , and super resolution together. A cyclic coordinate descent optimization procedure is used to solve the MAP formulation, in which the motion fields, fields, and HR images are found in an alternate manner given the two others, respectively. Specifically, the gradient-based methods are employed to solve the HR image and motion fields, and an iterated conditional mode optimization method to obtain the fields. The proposed algorithm has been tested using a synthetic image sequence, the "Mobile and Calendar" sequence, and the original "Motorcycle and Car" sequence. The experiment results and error analyses verify the efficacy of this algorithm.

Abstract This paper presents a new variational gradient-based fusion method for visible and short-wave infrared (SWIR) imagery. The proposed method enables spatial enhancement and dehazing of visible imagery. Integrating gradients from SWIR imagery into visible imagery produces a single image with true color and sharp gradients. A constraint based on band correlation is included to improve the enhancement and implement dehazing. The band correlation is according to the quantitative relationship between the wavelength and the atmospheric effect caused by Rayleigh scattering. In this study, both clear and hazy Landsat ETM[H11001] images are used in the experiments. By visual assessment, the gradient of the fused image is more salient than that of the original image, and the true color is well preserved. With the inclusion of the band correlation constraint, the proposed fusion method yields almost haze-free results. Quantitatively, the Metric Q of the fused images is significantly higher than that of the original images; the largest increase of the Metric Q in the experimental results is from 0.0114 to 0.0611. Moreover, for the results of the proposed method, the Metric Q increase in the visible bands declines from blue band to red band.

In this article, we have reviewed the reasons why we (collectively) want to love or leave the venerable (but perhaps hoary) MSE. We have also reviewed emerging alternative signal fidelity measures and discussed their potential application to a wide variety of problems. The message we are trying to send here is not that one should abandon use of the MSE nor to blindly switch to any other particular signal fidelity measure. Rather, we hope to make the point that there are powerful, easy-to-use, and easy-to-understand alternatives that might be deployed depending on the application environment and needs. While we expect (and indeed, hope) that the MSE will continue to be widely used as a signal fidelity measure, it is our greater desire to see more advanced signal fidelity measures being used, especially in applications where perceptual criteria might be relevant. Ideally, the performance of a new signal processing algorithm might be compared to other algorithms using several fidelity criteria. Lastly, we hope that we have given further motivation to the community to consider recent advanced signal fidelity measures as design criteria for optimizing signal processing algorithms and systems. It is in this direction that we believe that the greatest benefit eventually lies.

Preservation of spectral information and enhancement of spatial resolution are regarded as important issues in remote sensing satellite image fusion. In previous research, various algorithms have been proposed. Although they have been successful, there are still some margins of spatial and spectral quality that can be improved. In addition, a new method that can be used for various types of sensors is required. In this paper, a new adaptive fusion method based on component substitution is proposed to merge a high-spatial-resolution panchromatic (PAN) image with a multispectral image. This method generates high-/low-resolution synthetic component images by partial replacement and uses statistical ratio-based high-frequency injection. Various remote sensing satellite images, such as IKONOS-2, QuickBird, LANDSAT ETM+, and SPOT-5, were employed in the evaluation. Experiments showed that this approach can resolve spectral distortion problems and successfully conserve the spatial information of a PAN image. Thus, the fused image obtained from the proposed method gave higher fusion quality than the images from some other methods. In addition, the proposed method worked efficiently with the different sensors considered in the evaluation.

Methods have been proposed to produce multispectral images with enhanced spatial resolution using one or more images of the same scene of better spatial resolution. Assuming that the main concern of the user is the quality of the transformation of the multispectral content when increasing the spatial resolution, this paper defines the properties of such enhanced multispectral images. It then proposes both a formal approach and some criteria to provide a quantitative assessment of the spectral quality of these products. Five sets of criteria are defined. They measure the performance of a method to synthesize the radiometry in a single spectral band as well as the multispectral information when increasing the spatial resolution. The influence of the type of landscape present in the scene upon the assessment of the quality is underlined, as well as its dependence with scale. The whole approach is illustrated by the case of a SPOT image and three different standard methods to enhance the spatial resolution