The effect of the point spread function on sub-pixel mapping

REMOTE SENSING OF ENVIRONMENT

卷:193

Article

Wang, Qunming¹  Atkinson, Peter M.^1,2,3 

[1] Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England

[2] Univ Southampton, Geog & Environm, Southampton SO17 1BJ, Hants, England

[3] Queens Univ Belfast, Sch Geog Archaeol & Palaeoecol, Belfast BT7 1NN, Antrim, North Ireland

关键词: Land cover mapping;    Downscaling;    Sub-pixel mapping (SPM);    Super-resolution mapping;    Point spread function (PSF);    Hopfield neural network (HNN);   

DOI  :  10.1016/j.rse.2017.03.002

来源: Elsevier

【 摘 要 】

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. (C) 2017 Elsevier Inc. All rights reserved.

【 授权许可】

Free