TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 14, No 2: June 2016

Remote Sensing Image Fusion Scheme using Directional Vector in NSCT Domain

Baohui Tian (Henan Vocational & Technical College of Communications)
Lan Lan (Henan Vocational & Technical College of Communications)
Hailiang Shi (Zhengzhou University of Light Industry)
Yunxia Pei (Zhengzhou University of Light Industry)



Article Info

Publish Date
01 Jun 2016

Abstract

This paper is under in-depth investigation due to suspicion of possible plagiarism on a high similarity indexA novel remote sensing image fusion scheme is presented for panchromatic and multispectral images, which is based on NonSubsampled Contourlet Transform (NSCT) and Principal Component Analysis (PCA). The fusion principles of the different subband coefficients obtained by the NSCT decomposition are discussed in detail. A PCA-based weighted average principle is presented for the lowpass subbands, and a selection principle based on the variance of the directional vector is presented for the bandpass directional subbands, in which the directional vector is assembled by the NSCT coefficients of the different directional subbands but the same coordinate. The proposed scheme is tested on two sets of remote sensing images and compared with some traditional multiscale transform-based image fusion methods, such as discrete wavelet transform, stationary wavelet transform, dual-tree complex wavelet transform, contourlet transform. Experimental results demonstrate that the proposed scheme provides superior fused image in terms of several relevant quantitative fusion evaluation indexes.

Copyrights © 2016






Journal Info

Abbrev

TELKOMNIKA

Publisher

Subject

Computer Science & IT

Description

Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of ...