Fusion of multispectral and panchromatic images based on support value transform and adaptive principal component analysis
In this paper we combined the projection-substitution with ARSIS (French acronym for "Amelioration de la Resolution Spatiale par Injection de Structures", i.e., Improving Spatial Resolution by Structure Injection) concept assumption for fusion of panchromatic (PAN) and multispectral (MS) images. Firstly support value filter (SVF) is used to establish a new multiscale model (MSM), support vector transform (SVT), and adaptive principal component analysis (APCA) is then employed to select the principal components of MS images by means of a statistical measure of the correlation between MS and PAN images; secondly, a local approach is used to check whether a structure should appear in the new principal component and PAN high frequency structures are transformed by high resolution interband structure model (HRIBSM) before inserting in the MS modalities. Because SVT is an undecimated, dyadic and aliasing transform with shift-invariant property, the fused image can avoid ringing effects suffered from sampling. Additionally, the ARSIS concept can make full use of the remote sensing physics to reduce the spatial and spectrum distortion in the structure injection. Texture extraction is also employed to avoid the spectral distortion caused by the mistaken injection of low-pass components into the MS images. Experimental results including visual and numerical evaluation also proves the superiority of the proposed method to its counterparts. (C) 2010 Elsevier B.V. All rights reserved.
fusion support value transform arsis texture extraction adaptive pca