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

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