Remote Sensing Image Fusion Scheme using Directional Vector in NSCT Domain
Abstract: A 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.
Keywords: Image Fusion, Remote
Sensing, Nonsubsampled Contourlet Transform, Principal Eigenvector, Directional
Vector
Author: Baohui Tian, Lan Lan,
Hailiang Shi, Yunxia Pei
Journal Code: jptkomputergg160221
