16 June 2016 Remote sensing image fusion method in CIELab color space using nonsubsampled shearlet transform and pulse coupled neural networks
Xin Jin, Dongming Zhou, Shaowen Yao, Rencan Nie, Chuanbo Yu, Tingting Ding
Author Affiliations +
Abstract
In CIELab color space, we propose a remote sensing image fusion technique based on nonsubsampled shearlet transform (NSST) and pulse coupled neural network (PCNN), which aim to improve the efficiency and performance of the remote sensing image fusion by combining the excellent properties of the two methods. First, panchromatic (PAN) and multispectral (MS) are transformed into CIELab color space to get different color components. Second, PAN and L component of MS are decomposed by the NSST to obtain corresponding the low-frequency coefficients and high-frequency coefficients. Third, the low-frequency coefficients are fused by intersecting cortical model (ICM); the high-frequency coefficients are divided into several sub-blocks to calculate the average gradient (AG), and the linking strength β of PCNN model is determined by the AG, so that the parameters β can be adaptively set according to the quality of the sub-block images, then the sub-blocks image are input into PCNN to get the oscillation frequency graph (OFG), the method can get the fused high-frequency coefficients according to the OFG. Finally, the fused L component is obtained by inverse NSST, and the fused RGB color image is obtained through inverse CIELab transform. The experimental results demonstrate that the proposed method provide better effect compared with other common methods.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2016/$25.00 © 2016 SPIE
Xin Jin, Dongming Zhou, Shaowen Yao, Rencan Nie, Chuanbo Yu, and Tingting Ding "Remote sensing image fusion method in CIELab color space using nonsubsampled shearlet transform and pulse coupled neural networks," Journal of Applied Remote Sensing 10(2), 025023 (16 June 2016). https://doi.org/10.1117/1.JRS.10.025023
Published: 16 June 2016
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Cited by 27 scholarly publications.
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KEYWORDS
Image fusion

Remote sensing

Neurons

RGB color model

Neural networks

Silver

Image quality

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