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Remote Sens. 2017, 9(4), 316; doi:10.3390/rs9040316

Texture-Guided Multisensor Superresolution for Remotely Sensed Images

1
Department of Advanced Interdisciplinary Studies, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan
2
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, 82234 Wessling, Germany
3
Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany
Academic Editors: Jonathan Cheung-Wai Chan, Yongqiang Zhao, Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 4 January 2017 / Revised: 14 March 2017 / Accepted: 24 March 2017 / Published: 28 March 2017
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
View Full-Text   |   Download PDF [6236 KB, uploaded 28 March 2017]   |  

Abstract

This paper presents a novel technique, namely texture-guided multisensor superresolution (TGMS), for fusing a pair of multisensor multiresolution images to enhance the spatial resolution of a lower-resolution data source. TGMS is based on multiresolution analysis, taking object structures and image textures in the higher-resolution image into consideration. TGMS is designed to be robust against misregistration and the resolution ratio and applicable to a wide variety of multisensor superresolution problems in remote sensing. The proposed methodology is applied to six different types of multisensor superresolution, which fuse the following image pairs: multispectral and panchromatic images, hyperspectral and panchromatic images, hyperspectral and multispectral images, optical and synthetic aperture radar images, thermal-hyperspectral and RGB images, and digital elevation model and multispectral images. The experimental results demonstrate the effectiveness and high general versatility of TGMS. View Full-Text
Keywords: multisensor superresolution; texture guidance; multiresolution analysis; multiscale gradient descent multisensor superresolution; texture guidance; multiresolution analysis; multiscale gradient descent
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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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Yokoya, N. Texture-Guided Multisensor Superresolution for Remotely Sensed Images. Remote Sens. 2017, 9, 316.

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