Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33101
Title: Mixture analysis by multichannel hopfield neural network
Authors: Mei, S
He, M
Wang, Z
Feng, D
Keywords: Hopfield neural network (HNN)
linear mixture model (LMM)
mixed pixel unmixing
mixture analysis
remote sensing
Issue Date: 2010
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE geoscience and remote sensing letters, 2010, v. 7, no. 3, 5422642, p. 455-459 How to cite?
Journal: IEEE geoscience and remote sensing letters 
Abstract: Due to the spatial-resolution limitation, mixed pixels containing energy reflected from more than one type of ground objects are widely present in remote sensing images, which often results in inefficient quantitative analysis. To effectively decompose such mixtures, a fully constrained linear unmixing algorithm based on a multichannel Hopfield neural network (MHNN) is proposed in this letter. The proposed MHNN algorithm is actually a Hopfield-based architecture which handles all the pixels in an image synchronously, instead of considering a per-pixel procedure. Due to the synchronous unmixing property of MHNN, a noise energy percentage (NEP) stopping criterion which utilizes the signal-to-noise ratio is proposed to obtain optimal results for different applications automatically. Experimental results demonstrate that the proposed multichannel structure makes the Hopfield-based mixture analysis feasible for real-world applications with acceptable time cost. It has also been observed that the proposed MHNN-based mixture-analysis algorithm outperforms the other two popular linear mixture-analysis algorithms and that the NEP stopping criterion can approach optimal unmixing results adaptively and accurately.
URI: http://hdl.handle.net/10397/33101
ISSN: 1545-598X
DOI: 10.1109/LGRS.2009.2039114
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

13
Last Week
0
Last month
0
Citations as of Aug 13, 2017

WEB OF SCIENCETM
Citations

9
Last Week
0
Last month
0
Citations as of Aug 16, 2017

Page view(s)

22
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.