An iterative interpolation deconvolution algorithm for superresolution land cover mapping

Ling, Feng and Foody, Giles M. and Ge, Yong and Li, Xiaodong and Du, Yun (2016) An iterative interpolation deconvolution algorithm for superresolution land cover mapping. IEEE Transactions on Geoscience and Remote Sensing, 54 (12). pp. 7210-7222. ISSN 0196-2892

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Abstract

Super-resolution mapping (SRM) is a method to produce a fine spatial resolution land cover map from coarse spatial resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of coarse fraction images by interpolation, and then determines class labels of fine resolution pixels using the maximum a posteriori (MAP) principle. By constructing a new image formation process that establishes the relationship between observed coarse resolution fraction images and the latent fine resolution land cover map, it is found that the MAP principle only matches with area-to-point interpolation algorithms, and should be replaced by de-convolution if an area-to-area interpolation algorithm is to be applied. A novel iterative interpolation de-convolution (IID) SRM algorithm is proposed. The IID algorithm first interpolates coarse resolution fraction images with an area-to-area interpolation algorithm, and produces an initial fine resolution land cover map by de-convolution. The fine spatial resolution land cover map is then updated by re-convolution, back-projection and de-convolution iteratively until the final result is produced. The IID algorithm was evaluated with simulated shapes, simulated multi-spectral images, and degraded Landsat images, including comparison against three widely used SRM algorithms: pixel swapping, bilinear interpolation, and Hopfield neural network. Results show that the IID algorithm can reduce the impact of fraction errors, and can preserve the patch continuity and the patch boundary smoothness, simultaneously. Moreover, the IID algorithm produced fine resolution land cover maps with higher accuracies than those produced by other SRM algorithms.

Item Type: Article
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Interpolation, De-convolution, Super-resolution Mapping
Schools/Departments: University of Nottingham, UK > Faculty of Social Sciences > School of Geography
Identification Number: https://doi.org/10.1109/TGRS.2016.2598534
Depositing User: Eprints, Support
Date Deposited: 26 Oct 2016 08:40
Last Modified: 28 Oct 2016 16:25
URI: http://eprints.nottingham.ac.uk/id/eprint/37923

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