31 October 2018 Temporal-angular fusion of MODIS and MISR images via sparse representation
Huihui Song, Renlong Hang, Qingshan Liu, Guojie Wang, Wei Lian
Author Affiliations +
Abstract
Considering the complementarity of Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) images on temporal and angular resolutions, we propose a fusion method to generate frequent time series MISR images. Thereby, the fusion results can give full play to their respective advantages in the inversion of surface or atmospheric parameters. Based on sparse representation, the proposed method includes two stages: the spectral dictionary-pair training stage and the MISR image prediction stage. In the training stage, we establish a corresponding relationship between the basic MODIS and MISR representation atoms in the spectral domain by learning a dictionary pair from the prior image pairs. In the prediction stage, the MISR images are predicted from the corresponding MODIS images via sparse coding. Experimental results on Baltimore–Washington, DC metropolitan area demonstrate the effectiveness of the proposed method with approximately 7% prediction errors.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2018/$25.00 © 2018 SPIE
Huihui Song, Renlong Hang, Qingshan Liu, Guojie Wang, and Wei Lian "Temporal-angular fusion of MODIS and MISR images via sparse representation," Journal of Applied Remote Sensing 13(2), 022004 (31 October 2018). https://doi.org/10.1117/1.JRS.13.022004
Received: 17 August 2018; Accepted: 5 October 2018; Published: 31 October 2018
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Cited by 1 scholarly publication.
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KEYWORDS
MODIS

Image fusion

Associative arrays

Chemical species

Data fusion

Spatial resolution

Image compression

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