Sub-pixel mapping based on BP neural network with multiple shifted remote sensing images

Wen Zhong Shi, Yuan Ling Zhao, Qun Ming Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)


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.
Original languageChinese (Simplified)
Pages (from-to)527-532
Number of pages6
JournalHongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves
Issue number5
Publication statusPublished - 1 Jan 2014


  • Back-propagation neural network (BPNN)
  • Multiple shifted images
  • Remote sensing images
  • Sub-pixel mapping

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

Cite this