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Comparison of pansharpening algorithms: outcome of the 2006 GRS-S data fusion contest
- IEEE Transactions on Geoscience and Remote Sensing
, 2007
"... IEEE Geoscience and Remote Sensing Society launched a public contest for pansharpening algorithms, which aimed to identify the ones that perform best. Seven research groups worldwide par-ticipated in the contest, testing eight algorithms following differ-ent philosophies [component substitution, mul ..."
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Cited by 66 (17 self)
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IEEE Geoscience and Remote Sensing Society launched a public contest for pansharpening algorithms, which aimed to identify the ones that perform best. Seven research groups worldwide par-ticipated in the contest, testing eight algorithms following differ-ent philosophies [component substitution, multiresolution analysis (MRA), detail injection, etc.]. Several complete data sets from two different sensors, namely, QuickBird and simulated Pléiades, were delivered to all participants. The fusion results were collected and evaluated, both visually and objectively. Quantitative results of pansharpening were possible owing to the availability of reference originals obtained either by simulating the data collected from the satellite sensor by means of higher resolution data from an airborne platform, in the case of the Pléiades data, or by first degrading all the available data to a coarser resolution and saving the original as the reference, in the case of the QuickBird data. The evaluation results were presented during the special session on Data Fusion at the 2006 International Geoscience and Remote Sensing Symposium in Denver, and these are discussed in further detail in this paper. Two algorithms outperform all the others, the visual analysis being confirmed by the quantitative evaluation. These two methods share the same philosophy: they basically rely on MRA and employ adaptive models for the injection of high-pass details. Index Terms—Image fusion, multispectral (MS) imagery, pansharpening, quality assessment, QuickBird (QB), simulated Pléiades data. I.
Synthesis of multispectral images to high spatial resolution: A critical review of fusion methods based on remote sensing physics
- IEEE Trans. Geosci. Remote Sensing
, 2008
"... Abstract—Our framework is the synthesis of multispectral images (MS) at higher spatial resolution, which should be as close as possible to those that would have been acquired by the corresponding sensors if they had this high resolution. This synthesis is performed with the help of a high spatial bu ..."
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Cited by 35 (8 self)
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Abstract—Our framework is the synthesis of multispectral images (MS) at higher spatial resolution, which should be as close as possible to those that would have been acquired by the corresponding sensors if they had this high resolution. This synthesis is performed with the help of a high spatial but low spectral resolution image: the panchromatic (Pan) image. The fusion of the Pan and MS images is classically referred as pan-sharpening. A fused product reaches good quality only if the characteristics and differences between input images are taken into account. Dissimilarities existing between these two data sets originate from two causes—different times and different spectral bands of acquisition. Remote sensing physics should be carefully considered while designing the fusion process. Because of the complexity of physics and the large number of unknowns, authors are led to make assumptions to drive their development. Weaknesses and strengths of each reported method are raised and confronted to these physical constraints. The conclusion of this critical survey of literature is that the choice in the assumptions for the development of a method is crucial, with the risk to drastically weaken fusion performance. It is also shown that the Amélioration de la Résolution Spatiale par Injection de Structures concept prevents from introducing spectral distortion into fused products and offers a reliable framework for further developments. Index Terms—Image enhancement, image processing, merging, multiresolution techniques, remote sensing. I.
Multispectral Visible and Infrared Imaging for Face Recognition
"... Multispectral imaging in the visible and near infrared spectra helps reduce color variations in the face due to changes in illumination source types and directions. Thermal infrared imaging provides useful signatures of the face that is insensitive to ambient lighting through the measurement of heat ..."
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Cited by 12 (2 self)
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Multispectral imaging in the visible and near infrared spectra helps reduce color variations in the face due to changes in illumination source types and directions. Thermal infrared imaging provides useful signatures of the face that is insensitive to ambient lighting through the measurement of heat energy radiated from the object. This paper introduces the use of multispectral imaging and thermal infrared imaging as alternative means to conventional broadband monochrome or color imaging sensors in order to enhance the performance of face recognition in uncontrolled illumination conditions. 1.
Methods for Image fusion Quality Assessment –A Review , Comparison and Analysis‖ The international achieves of the photogrammetry , Remote sensing information sciences vol. XXXXVII Part b7 Beijing 2008
"... This paper focuses on the evaluation and analysis of seven frequently used image fusion quality assessment methods to see whether, or not, they can provide convincing image quality or similarity measurements. The seven indexes are Mean Bias (MB), Variance ..."
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Cited by 3 (0 self)
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This paper focuses on the evaluation and analysis of seven frequently used image fusion quality assessment methods to see whether, or not, they can provide convincing image quality or similarity measurements. The seven indexes are Mean Bias (MB), Variance
Super-Resolution Reconstruction Algorithm To MODIS Remote Sensing Images
, 2007
"... In this paper, we propose a super-resolution image reconstruction algorithm to moderate-resolution imaging spectroradiometer (MODIS) remote sensing images. This algorithm consists of two parts: registration and reconstruction. In the registration part, a truncated quadratic cost function is used to ..."
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Cited by 3 (1 self)
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In this paper, we propose a super-resolution image reconstruction algorithm to moderate-resolution imaging spectroradiometer (MODIS) remote sensing images. This algorithm consists of two parts: registration and reconstruction. In the registration part, a truncated quadratic cost function is used to exclude the outlier pixels, which strongly deviate from the registration model. Accurate photometric and geometric registration parameters can be obtained simultaneously. In the reconstruction part, the L1 norm data fidelity term is chosen to reduce the effects of inevitable registration error, and a Huber prior is used as regularization to preserve sharp edges in the reconstructed image. In this process, the outliers are excluded again to enhance the robustness of the algorithm. The proposed algorithm has been tested using real MODIS band-4 images, which were captured in different dates. The experimental results and comparative analyses verify the effectiveness of this algorithm.
Objective quality assessment for multiexposure multifocus image fusion
- Department of Electrical and Computer Engineering, University of Waterloo, Canada. His
, 2015
"... Abstract — There has been a growing interest in image fusion technologies, but how to objectively evaluate the quality of fused images has not been fully understood. Here, we propose a method for objective quality assessment of multiexposure multifocus image fusion based on the evaluation of three k ..."
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Abstract — There has been a growing interest in image fusion technologies, but how to objectively evaluate the quality of fused images has not been fully understood. Here, we propose a method for objective quality assessment of multiexposure multifocus image fusion based on the evaluation of three key factors of fused image quality: 1) contrast preservation; 2) sharpness; and 3) structure preservation. Subjective experiments are conducted to create an image fusion database, based on which, performance evaluation shows that the proposed fusion quality index correlates well with subjective scores, and gives a significant improvement over the existing fusion quality measures. Index Terms — Image fusion, image quality assessment, local phase coherence, multi-focus image fusion, multi-exposure image fusion. I.
Image Fusion With No Gamut Problem by Improved Nonlinear IHS Transforms for Remote Sensing
- IEEE Transactions on Geoscience And Remote Sensing
, 2014
"... Abstract—An image fusion method must ideally preserve both the detail of the panchromatic image and the color of the mul-tispectral image. Existing image fusion methods incur the gamut problem of creating new colors which fall out of the RGB cube. These methods solve the problem by color clipping wh ..."
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Cited by 3 (0 self)
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Abstract—An image fusion method must ideally preserve both the detail of the panchromatic image and the color of the mul-tispectral image. Existing image fusion methods incur the gamut problem of creating new colors which fall out of the RGB cube. These methods solve the problem by color clipping which yields undesirable color distortions and contrast reductions. An im-proved nonlinear IHS (intensity, hue, saturation; iNIHS) color space and related color transformations are proposed in this paper to solve the gamut problem without appealing to color clipping. The iNIHS space includes two halves, one being constructed from the lower half of the RGB cube by RGB to IHS transformations, and the other from the upper half of the RGB cube by CMY to IHS transformations. While incurring no out-of-gamut colors, desired intensity substitutions and additions in substitutive and additive image fusions, respectively, are all achievable, with the saturation component regulated within the maximum attainable range. Good experimental results show the feasibility of the proposed method. Index Terms—Clipping, gamut problem, image fusion, im-proved nonlinear IHS (iNIHS) color space, multispectral image, panchromatic image. I.
A regularized model-based optimization framework for pan-sharpening
- IEEE Trans. Image Proc
"... Abstract — Pan-sharpening is a common postprocessing oper-ation for captured multispectral satellite imagery, where the spatial resolution of images gathered in various spectral bands is enhanced by fusing them with a panchromatic image captured at a higher resolution. In this paper, pan-sharpening ..."
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Cited by 3 (1 self)
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Abstract — Pan-sharpening is a common postprocessing oper-ation for captured multispectral satellite imagery, where the spatial resolution of images gathered in various spectral bands is enhanced by fusing them with a panchromatic image captured at a higher resolution. In this paper, pan-sharpening is formulated as the problem of jointly estimating the high-resolution (HR) multispectral images to minimize an objective function comprised of the sum of squared residual errors in physically motivated observation models of the low-resolution (LR) multispectral and the HR panchromatic images and a correlation-dependent regu-larization term. The objective function differs from and improves upon previously reported model-based optimization approaches to pan-sharpening in two major aspects: 1) a new regularization term is introduced and 2) a highpass filter, complementary to the lowpass filter for the LR spectral observations, is introduced for the residual error corresponding to the panchromatic observation model. To obtain pan-sharpened images, an iterative algorithm is developed to solve the proposed joint minimization. The proposed algorithm is compared with previously proposed methods both visually and using established quantitative measures of SNR, spectral angle mapper, relative dimensionless global error in synthesis, Q, and Q4 indices. Both the quantitative results and visual evaluation demonstrate that the proposed joint formulation provides superior results compared with pre-existing methods. A software implementation is provided. Index Terms — Pan-sharpening, satellite imagery, image fusion, spectral imaging. I.
The tradeoff analysis for remote sensing image fusion using expanded spectral angle mapper
- Sensors
"... Abstract: Image fusion is a useful tool in integrating a high-resolution panchromatic image (HRPI) with a low-resolution multispectral image (LRMI) to produce a highresolution multispectral image (HRMI). To date, many image fusion techniques have been developed to try to improve the spatial resoluti ..."
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Cited by 2 (1 self)
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Abstract: Image fusion is a useful tool in integrating a high-resolution panchromatic image (HRPI) with a low-resolution multispectral image (LRMI) to produce a highresolution multispectral image (HRMI). To date, many image fusion techniques have been developed to try to improve the spatial resolution of the LRMI to that of the HRPI with its spectral property reliably preserved. However, many studies have indicated that there exists a trade- off between the spatial resolution improvement and the spectral property preservation of the LRMI, and it is difficult for the existing methods to do the best in both aspects. Based on one minimization problem, this paper mathematically analyzes the tradeoff in fusing remote sensing images. In experiment, four fusion methods are evaluated through expanded spectral angle mapper (ESAM). Results clearly prove that all the tested methods have this property.
Fusing continuous spectral images for face recognition
"... under indoor and outdoor illuminants ..."
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