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Remote Sens. 2017, 9(1), 21; doi:10.3390/rs9010021

Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

1
Institute of Space Science and Technology, Nanchang University, Nanchang 330031, China
2
School of Computer Science, China University of Geoscience, Wuhan 430074, China
3
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Academic Editors: Guoqing Zhou and Prasad S. Thenkabail
Received: 22 October 2016 / Revised: 18 December 2016 / Accepted: 28 December 2016 / Published: 30 December 2016
View Full-Text   |   Download PDF [1369 KB, uploaded 30 December 2016]   |  

Abstract

Fusion of remote sensing images with different spatial and temporal resolutions is highly needed by diverse earth observation applications. A small number of spatiotemporal fusion methods using sparse representation appear to be more promising than traditional linear mixture methods in reflecting abruptly changing terrestrial content. However, one of the main difficulties is that the results of sparse representation have reduced expressional accuracy; this is due in part to insufficient prior knowledge. For remote sensing images, the cluster and joint structural sparsity of the sparse coefficients could be employed as a priori knowledge. In this paper, a new optimization model is constructed with the semi-coupled dictionary learning and structural sparsity to predict the unknown high-resolution image from known images. Specifically, the intra-block correlation and cluster-structured sparsity are considered for single-channel reconstruction, and the inter-band similarity of joint-structured sparsity is considered for multichannel reconstruction, and both are implemented with block sparse Bayesian learning. The detailed optimization steps are given iteratively. In the experimental procedure, the red, green, and near-infrared bands of Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS) satellites are put to fusion with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than state-of-the-art methods. View Full-Text
Keywords: remote sensing; image fusion; Landsat-7; MODIS; spatiotemporal fusion; dictionary learning; reflectance; structural sparsity remote sensing; image fusion; Landsat-7; MODIS; spatiotemporal fusion; dictionary learning; reflectance; structural sparsity
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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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Wei, J.; Wang, L.; Liu, P.; Song, W. Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning. Remote Sens. 2017, 9, 21.

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