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ISPRS Journal of Photogrammetry and Remote sensing
Sparsity inspired pan-sharpening technique using multi-scale learned dictionary2018 •
The significant issues in remote sensing image fusion are enhancing the spatial details and preserving the essential spectral information. The classical pan-sharpening methods often incur spectral distortion and still striving to produce the fused images with prominent spatial and spectral attributes. Motivated by the desirable results of sparse representation (SR) theory, a novel pan-sharpening method is developed based on SR of high frequency (HF) components over a multi-scale learned dictionary (MSLD). MSLD technique acquires the capability of extracting the intrinsic characteristics of images, wherein, it possess the features of both multi-scale representation and learned dictionaries. In this paper, the dictionaries are adaptively learned from HF sub-images derived from the two versions of panchromatic image, realized at different spatial resolutions. A fast and computationally efficient algorithm is used for dictionary learning. The notion of SR together with patch recurrence over different scales is incorporated to estimate the high frequency details. The fused image is reconstructed by injecting the band specific spatial details into the up-sampled multi-spectral images. The performance of the proposed method is appraised with the datasets from different satellite sensors namely, QuickBird, IKONOS, WorldView-2 and Pléiades. The observations inferred from visual perception and quality indices analysis manifest the efficiency of proposed method over several well-known methods for the datasets considered at reduced-scale and full-scale resolutions. Further, the quantitative analysis of obtained performance measures confirms the efficacy of the proposed method for the reduced-scale and full-scale data sets. Especially, at a reduced-scale, proposed method yields an optimal value of Correlation coefficient, Structural similarity and Q4. In a comparative sense, usage of the proposed method at full-scale results in 4% and 2.56% improvement in the Spatial distortion index for QuickBird and WorldView-2 data respectively contrary to the best reported outcome obtained from Sparse Representation of injected details (SR-D) scheme. Invariably, for full-scale data, the QNR attains its optimal value.
EURASIP Journal on Advances in Signal Processing
A survey of classical methods and new trends in pansharpening of multispectral images2011 •
There exist a number of satellites on different earth observation platforms, which provide multispectral images together with a panchromatic image, that is, an image containing reflectance data representative of a wide range of bands and wavelengths. Pansharpening is a pixel-level fusion technique used to increase the spatial resolution of the multispectral image while simultaneously preserving its spectral information. In this paper, we provide a review of the pan-sharpening methods proposed in the literature giving a clear classification of them and a description of their main characteristics. Finally, we analyze how the quality of the pansharpened images can be assessed both visually and quantitatively and examine the different quality measures proposed for that purpose.
2014 •
Remote sensing image fusion is an effective way to use a large volume of data from multisensor images. Most earth satellites such as SPOT, Landsat 7, IKONOS and QuickBird provide both panchromatic (Pan) images at a higher spatial resolution and multispectral (MS) images at a lower spatial resolution and many remote sensing applications require both high spatial and high spectral resolutions, especially for GIS based applications. An effective image fusion technique can produce such remotely sensed images. Image fusion is the combination of two or more different images to form a new image by using a certain algorithm to obtain more and better information about an object or a study area than. The image fusion is performed at three different processing levels which are pixel level, feature level and decision level according to the stage at which the fusion takes place. There are many image fusion methods that can be used to produce high resolution multispectral images from a high resol...
2011 •
Journal of Electronic Imaging
Review of multiscale geometric decompositions in a remote sensing context2016 •
International Journal of Sensors and Sensor Networks
A Survey of Multi Sensor Satellite Image Fusion Techniques2020 •
Multi sensor image fusion is the technique used to combine heterogeneous images of the same scene obtained using different sensors. The objective of image fusion is to produce a single image containing the best aspects of the fused images. Some desirable aspects of Image Fusion include high spatial resolution and high spectral resolution (multispectral and panchromatic satellite images), areas in focus (microscopy images), functional and anatomic information (medical images), different spectral information (optical and infrared images), or color information and texture information (multispectral and synthetic aperture radar images). Image fusion can also be used for providing some protection against illegal copying by embedding watermarks. For all of the schemes, it is assumed that the images have been co-registered and resampled. The aim of this survey is to present a review of publications related to Multi Sensor Image Fusion. This paper paints a comprehensive picture of Multi Sensor Image Fusion methods and their applications. This paper is an introduction for those new to the field, an overview for those working in the field and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of Multi Sensor Image Fusion.
IEEE Transactions on Geoscience and Remote Sensing
Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest2000 •
IEEE Geoscience and Remote Sensing Letters
An Optimized Approach for Pansharpening Very High Resolution Multispectral Images2000 •
IEEE Transactions on Geoscience and Remote Sensing
Improving Component Substitution Pansharpening Through Multivariate Regression of MS +Pan Data2007 •
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
A Variational Approach for Fusion of Panchromatic and Multispectral Images Using a New Spatial–Spectral Consistency TermIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
A Robust Pansharpening Algorithm Based on Convolutional Sparse Coding for Spatial Enhancement2019 •
IEEE Journal of Selected Topics in Signal Processing
A Theoretical Analysis of the Effects of Aliasing and Misregistration on Pansharpened Imagery2000 •
IEEE Transactions on Geoscience and Remote Sensing
Noise-Resistant Wavelet-Based Bayesian Fusion of Multispectral and Hyperspectral Images2000 •
2010 IEEE International Conference on Image Processing
Pansharpening with a decision fusion based on the local size information2010 •
International Journal of Remote Sensing
Efficient pan-sharpening of satellite images with the contourlet transform2014 •
IEEE Geoscience and Remote Sensing Magazine
Hyperspectral Pansharpening: A Review2015 •
2010 •
IEEE Transactions on Geoscience and Remote Sensing
Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics2000 •
Advances in Image and Video Processing
Spectral and Spatial Quality assessment of IHS and Wavelet Based Pan-sharpening Techniques for High Resolution Satellite ImageryImage and Signal Processing for Remote Sensing XVII
<title>Multispectral pansharpening based on pixel modulation: state of the art and new results</title>2011 •
… and Remote Sensing …
Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods2005 •
Journal of Marine Science and Engineering
PlanetScope and Landsat 8 Imageries for Bathymetry MappingRemote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII
<title>Quality assessment for multitemporal and multisensor image fusion</title>2008 •
IEEE Transactions on Geoscience and Remote Sensing
Pansharpening Quality Assessment Using the Modulation Transfer Functions of Instruments2000 •
IEEE Transactions on Geoscience and Remote Sensing
A Critical Comparison Among Pansharpening Algorithms2015 •
Arabian Journal of Geosciences
Object-based spectral quality assessment of high-resolution pan-sharpened satellite imageries: new combined fusion strategy to increase the spectral quality2020 •
2013 •
IEEE Geoscience and Remote Sensing Letters
A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images2000 •
IEEE Transactions on Geoscience and Remote Sensing
Model-Based Fusion of Multi- and Hyperspectral Images Using PCA and Wavelets2015 •
Image Fusion and Its Applications
Image Fusion for Remote Sensing Applications2011 •
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
An Area-Based Image Fusion Scheme for the Integration of SAR and Optical Satellite Imagery2013 •
IEEE Transactions on Geoscience and Remote Sensing
Image Fusion Processing for IKONOS 1-m Color Imagery2000 •