eCite Digital Repository

Deconvolution of MODIS imagery using multiscale maximum entropy


Jackett, CJ and Turner, PJ and Lovell, JL and Williams, RN, Deconvolution of MODIS imagery using multiscale maximum entropy, Remote Sensing Letters, 2, (3) pp. 179-187. ISSN 2150-704X (2011) [Refereed Article]

Not available

Copyright Statement

Copyright 2011 CSIRO.

DOI: doi:10.1080/01431161.2010.486011


A multiscale maximum entropy method (MEM) for image deconvolution is implemented and applied to MODIS (moderate resolution imaging spectroradiometer) data to remove instrument point-spread function (PSF) effects. The implementation utilizes three efficient computational methods: a fast Fourier transform convolution, a wavelet image decomposition and an algorithm for gradient method step-size estimation that together enable rapid image deconvolution. Multiscale entropy uses wavelet transforms to implicitly include an image’s two-dimensional structural information into the algorithm’s entropy calculation. An evaluation using synthetic data shows that the deconvolution algorithm reduces the maximum individual pixel error from 90.01 to 0.34%. Deconvolution of MODIS data is shown to resolve significant features and is most effective in regions where there are large changes in radiance such as coastal zones or contrasting land covers.

Item Details

Item Type:Refereed Article
Research Division:Information and Computing Sciences
Research Group:Computer vision and multimedia computation
Research Field:Image processing
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Jackett, CJ (Mr Christopher Jackett)
UTAS Author:Williams, RN (Dr Ray Williams)
ID Code:74448
Year Published:2011
Web of Science® Times Cited:7
Deposited By:Information and Communication Technology
Deposited On:2011-12-01
Last Modified:2015-02-13

Repository Staff Only: item control page