Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12864
Title: Sub-pixel mapping based on BP neural network with multiple shifted remote sensing images
Authors: Shi, WZ 
Zhao, YL
Wang, QM
Keywords: Back-propagation neural network (BPNN)
Multiple shifted images
Remote sensing images
Sub-pixel mapping
Issue Date: 2014
Publisher: 中國學術期刊 (光盤版) 電子雜誌社
Source: 紅外與毫米波學報 (Journal of infrared and millimeter waves), 2014, v. 33, no. 5, p. 527-532 How to cite?
Journal: 紅外與毫米波學報 (Journal of infrared and millimeter waves) 
Abstract: A new sub-pixel mapping method is presented in this paper, which makes use of multiple shifted remote sensing images to enhance the back-propagation neural network (BPNN)-based sub-pixel mapping method. Different from the original BPNN method that uses a single observed coarse spatial resolution image, the new method integrates multiple coarse spatial resolution images that are shifted from each other to determine the probability of a sub-pixel belonging to each class. The probabilities and land cover fractions are then used to allocate classes for sub-pixels. The proposed method can decrease the uncertainty and errors in BPNN-based sub-pixel mapping. Experimental results show that with both visual and quantitative evaluation, the proposed method can obtain more accurate sub-pixel mapping results.
URI: http://hdl.handle.net/10397/12864
ISSN: 1001-9014
DOI: 10.3724/SP.J.1010.2014.00527
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