Fusion of multiparametric SAR images based on SW-nonsubsampled contourlet and PCNN

Authors:
Shuyuan Yang;Min Wang;YanXiong Lu;Weidong Qi;Licheng Jiao
Affiliations:
Department of Electrical Engineering, Institute of Intelligent Information Processing, Xidian University, Xi'an 710071, China;Department of Electrical Engineering, National Key Lab of Radar Signal Processing, Xidian University, Xi'an 710071, China;Department of Electrical Engineering, Institute of Intelligent Information Processing, Xidian University, Xi'an 710071, China;Department of Electrical Engineering, Institute of Intelligent Information Processing, Xidian University, Xi'an 710071, China;Department of Electrical Engineering, National Key Lab of Radar Signal Processing, Xidian University, Xi'an 710071, China
Venue:
Signal Processing
Year:
2009

Citing 6
Cited 5

Objective Image Fusion Performance Characterisation

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Multi-band SAR Images Fusion Using the EM Algorithm in Contourlet Domain

FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
A filter bank for the directional decomposition of images: theoryand design

IEEE Transactions on Signal Processing
The contourlet transform: an efficient directional multiresolution image representation

IEEE Transactions on Image Processing
The Nonsubsampled Contourlet Transform: Theory, Design, and Applications

IEEE Transactions on Image Processing
Physiologically motivated image fusion for object detection using a pulse coupled neural network

IEEE Transactions on Neural Networks

Image fusion using self-constraint pulse-coupled neural network

LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Contourlet hidden Markov Tree and clarity-saliency driven PCNN based remote sensing images fusion

Applied Soft Computing
Adaptive multi-focus image fusion using a wavelet-based statistical sharpness measure

Signal Processing
A new method for expert target recognition system: Genetic wavelet extreme learning machine (GAWELM)

Expert Systems with Applications: An International Journal
Review: Pulse coupled neural networks and its applications

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.08

Visualization

Abstract

In this paper, a new fusion rule based on a pulse coupled neural network (PCNN) and the clarity of images is proposed for multi-band synthetic aperture radar (SAR) image fusion. By using a stationary wavelet-based nonsubsampled contourlet transform (SW-NSCT), we can calculate a flexible multiscale, multidirectional, anisotropy and shift-invariant representation of registered SAR images. A weighted fusion rule is performed on the low frequency subbands to calculate the fused lowpass band. For the fusion of high frequency directional subband images, a PCNN model is constructed, where the linking strength of each neuron is determined by the clarity of the decomposed subband images. The fusion approach exploits the advantages of both SW-NSCT in multiscale geometric representations and that of PCNN in the determination of fusion rules; as predicted, the obtained fusion image can preserve much more information regarding textures and edges of the images, compared to its counterparts. Some experiments are performed by comparing the new algorithm with other existing fusion rules and methods. The experimental results show that the proposed fusion approach is effective and can provide better performance in fusing multi-band SAR images than some current methods.