Rights statement: This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 193, 2017 DOI: 10.1016/j.rse.2017.03.002
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - The effect of the point spread function on sub-pixel mapping
AU - Wang, Qunming
AU - Atkinson, Peter M.
N1 - This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 193, 2017 DOI: 10.1016/j.rse.2017.03.002
PY - 2017/5
Y1 - 2017/5
N2 - Abstract Sub-pixel mapping (SPM) is a process for predicting spatially the land cover classes within mixed pixels. In existing SPM methods, the effect of point spread function (PSF) has seldom been considered. In this paper, a generic SPM method is developed to consider the PSF effect in SPM and, thereby, to increase prediction accuracy. We first demonstrate that the spectral unmixing predictions (i.e., coarse land cover proportions used as input for SPM) are a convolution of not only sub-pixels within the coarse pixel, but also sub-pixels from neighboring coarse pixels. Based on this finding, a new SPM method based on optimization is developed which recognizes the optimal solution as the one that when convolved with the PSF, is the same as the input coarse land cover proportion. Experimental results on three separate datasets show that the SPM accuracy can be increased by considering the PSF effect.
AB - Abstract Sub-pixel mapping (SPM) is a process for predicting spatially the land cover classes within mixed pixels. In existing SPM methods, the effect of point spread function (PSF) has seldom been considered. In this paper, a generic SPM method is developed to consider the PSF effect in SPM and, thereby, to increase prediction accuracy. We first demonstrate that the spectral unmixing predictions (i.e., coarse land cover proportions used as input for SPM) are a convolution of not only sub-pixels within the coarse pixel, but also sub-pixels from neighboring coarse pixels. Based on this finding, a new SPM method based on optimization is developed which recognizes the optimal solution as the one that when convolved with the PSF, is the same as the input coarse land cover proportion. Experimental results on three separate datasets show that the SPM accuracy can be increased by considering the PSF effect.
KW - Land cover mapping
KW - Downscaling
KW - Sub-pixel mapping (SPM)
KW - Super-resolution mapping
KW - Point spread function (PSF)
KW - Hopfield neural network (HNN)
U2 - 10.1016/j.rse.2017.03.002
DO - 10.1016/j.rse.2017.03.002
M3 - Journal article
VL - 193
SP - 127
EP - 137
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
SN - 0034-4257
ER -