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Remote Sens. 2017, 9(7), 724; doi:10.3390/rs9070724

Super-Resolution Reconstruction of Remote Sensing Images Using Multiple-Point Statistics and Isometric Mapping

1
College of Computer Science and Technology, Shanghai University of Electric Power, 2588 Changyang Road, Shanghai 200090, China
2
School of Engineering, Shanghai Polytechnic University, 2360 Jinhai Road, Shanghai 201209, China
*
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz, Lizhe Wang, Sangram Ganguly and Richard Gloaguen
Received: 13 April 2017 / Revised: 6 July 2017 / Accepted: 10 July 2017 / Published: 15 July 2017
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

Abstract

When using coarse-resolution remote sensing images, super-resolution reconstruction is widely desired, and can be realized by reproducing the intrinsic features from a set of coarse-resolution fraction data to fine-resolution remote sensing images that are consistent with the coarse fraction information. Prior models of spatial structures that encode the expected features at the fine (target) resolution are helpful to constrain the spatial patterns of remote sensing images to be generated at that resolution. These prior models can be used properly by multiple-point statistics (MPS), capable of extracting the intrinsic features of patterns from prior models such as training images, and copying them to the simulated regions using hard and soft conditional data, or even without any conditional data. However, because traditional MPS methods based on linear dimensionality reduction are not suitable to deal with nonlinear data, and isometric mapping (ISOMAP) can reduce the dimensionality of nonlinear data effectively, this paper presents a sequential simulation framework for generating super-resolution remote sensing images using ISOMAP and MPS. Using four different examples, it is demonstrated that the structural characteristics of super-resolution reconstruction of remote sensing images using this method, are similar to those of training images. View Full-Text
Keywords: remote sensing images; dimensionality reduction; super-resolution reconstruction; soft data; training image remote sensing images; dimensionality reduction; super-resolution reconstruction; soft data; training image
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, T.; Du, Y.; Lu, F. Super-Resolution Reconstruction of Remote Sensing Images Using Multiple-Point Statistics and Isometric Mapping. Remote Sens. 2017, 9, 724.

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