Journal ArticleDOI
Hyper-Sharpening: A First Approach on SIM-GA Data
TLDR
This paper defines a new paradigm (hypersharpening) in remote sensing image fusion, and draws the readers' attention to its peculiar characteristics, by proposing and evaluating two hypersharpens methods.Abstract:
This paper aims at defining a new paradigm (hypersharpening) in remote sensing image fusion. In fact, due to the development of new instruments, thinking only in terms of pansharpening is reductive. Even though some expressions as hyperspectral (HS) pansharpening already exist, there is not a suitable definition when multispectral/hyperspectral data are used as source to extract spatial details. After defining the hypersharpening framework, we draw the readers’ attention to its peculiar characteristics, by proposing and evaluating two hypersharpening methods. Experiments are carried out on the data produced by the updated version of SIM-GA imager, designed by Selex ES, which is composed by a panchromatic camera and two spectrometers in the VNIR and SWIR spectral ranges, respectively. Owing to the different resolution factors among panchromatic, VNIR and SWIR data sets, we can apply hypersharpening to fuse SWIR data to VNIR resolution. Comparisons of hypersharpening with “traditional” pansharpening show hypersharpening is more effective.read more
Citations
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Journal ArticleDOI
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art
Pedram Ghamisi,Naoto Yokoya,Jun Li,Wenzhi Liao,Sicong Liu,Javier Plaza,Behnood Rasti,Antonio Plaza +7 more
TL;DR: Rigorous and innovative methodologies are required for hyperspectral image (HSI) and signal processing and have become a center of attention for researchers worldwide.
Journal ArticleDOI
Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature
TL;DR: Ten state-of-the-art HS-MS fusion methods are compared by assessing their fusion performance both quantitatively and visually and the generalizability and versatility of the fusion algorithms are evaluated.
Journal ArticleDOI
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art
Pedram Ghamisi,Behnood Rasti,Naoto Yokoya,Qunming Wang,Bernhard Höfle,Lorenzo Bruzzone,Francesca Bovolo,Mingmin Chi,Katharina Anders,Richard Gloaguen,Peter M. Atkinson,Jon Atli Benediktsson +11 more
TL;DR: An increase in remote sensing and ancillary data sets opens up the possibility of utilizing multimodal data sets in a joint manner to further improve the performance of the processing approaches with respect to applications at hand.
Proceedings ArticleDOI
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
TL;DR: Wang et al. as discussed by the authors proposed a model-based deep learning approach for merging an HrMS and LrHS images to generate a high-resolution hyperspectral (HrHS) image.
Posted Content
Multisource and Multitemporal Data Fusion in Remote Sensing.
Pedram Ghamisi,Behnood Rasti,Naoto Yokoya,Qunming Wang,Bernhard Höfle,Lorenzo Bruzzone,Francesca Bovolo,Mingmin Chi,Katharina Anders,Richard Gloaguen,Peter M. Atkinson,Jon Atli Benediktsson +11 more
TL;DR: In this paper, the authors provide a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references.
References
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Book
Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Book
Principal Component Analysis
TL;DR: In this article, the authors present a graphical representation of data using Principal Component Analysis (PCA) for time series and other non-independent data, as well as a generalization and adaptation of principal component analysis.
Journal ArticleDOI
A global geometric framework for nonlinear dimensionality reduction.
TL;DR: An approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set and efficiently computes a globally optimal solution, and is guaranteed to converge asymptotically to the true structure.
Journal ArticleDOI
A universal image quality index
Zhou Wang,Alan C. Bovik +1 more
TL;DR: Although the new index is mathematically defined and no human visual system model is explicitly employed, experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error.