Model based PCA/wavelet fusion of multispectral and hyperspectral images

Abstract

Due to cost and complexity issues, hyperspectral (HS) images have lower spatial resolution than multispectral (MS) and panchromatic (PAN) images. We present a novel method for fusing both MS and PAN images and also HS and MS images, based on their statistical properties in the wavelet domain. HS images contain spectral redundancy that makes the dimensionality reduction of the data via principal component analysis (PCA) very effective. The fusion is performed in the lower dimensional PC subspace so we only need to estimate the first few PCs, instead of every spectral reflectance band, and without compromising the spectral and spatial quality. The benefits of the approach are substantially lower computational requirements and a very high tolerance to noise in the observed data. Examples are presented using World View 2 data and also a simulated dataset based on a real HS image, with and without noise.