Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11019
Title: Land cover change detection at subpixel resolution with a Hopfield neural network
Authors: Wang, Q
Shi, W 
Atkinson, PM
Li, Z 
Keywords: Hopfield neural network (HNN)
Land cover change detection (LCCD)
Subpixel mapping (SPM)
Super-resolution mapping
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE Journal of selected topics in applied earth observations and remote sensing, 2015, v. 8, no. 3, 6906234, p. 1339-1352 How to cite?
Journal: IEEE journal of selected topics in applied earth observations and remote sensing 
Abstract: In this paper, a new subpixel resolution land cover change detection (LCCD) method based on the Hopfield neural network (HNN) is proposed. The new method borrows information from a known fine spatial resolution land cover map (FSRM) representing one date for subpixel mapping (SPM) from a coarse spatial resolution image on another, closer date. It is implemented by using the thematic information in the FSRM to modify the initialization of neuron values in the original HNN. The predicted SPM result was compared to the original FSRM to achieve subpixel resolution LCCD. The proposed method was compared with the original unmodified HNN method as well as six state-of-the-art methods for LCCD. To explore the effect of uncertainty in spectral unmixing, which mainly originates from spectral separability in the input, coarse image, and the point spread function (PSF) of the sensor, a set of synthetic multispectral images with different class separabilities and PSFs was used in experiments. It was found that the proposed LCCD method (i.e., HNN with an FSRM) can separate more real changes from noise and produce more accurate LCCD results than the state-of-the-art methods. The advantage of the proposed method is more evident when the class separability is small and the variance in the PSF is large, that is, the uncertainty in spectral unmixing is large. Furthermore, the utilization of an FSRM can expedite the HNN-based processing required for LCCD. The advantage of the proposed method was also validated by applying to a set of real Landsat-Moderate Resolution Imaging Spectroradiometer (MODIS) images.
URI: http://hdl.handle.net/10397/11019
ISSN: 1939-1404
EISSN: 2151-1535
DOI: 10.1109/JSTARS.2014.2355832
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

14
Last Week
1
Last month
0
Citations as of Aug 14, 2017

WEB OF SCIENCETM
Citations

15
Last Week
1
Last month
0
Citations as of Aug 15, 2017

Page view(s)

36
Last Week
1
Last month
Checked on Aug 13, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.