Electro-optical and SAR image fusion for improvements on target feature estimation

Abstract

In this paper, tradeoff studies on several pixel level fusion algorithms and on their performance evaluation criteria are presented. Electro-optical (EO) and SAR sensors are dissimilar and produce images with very low degrees of correlation. These images are initially registered at subpixel level accuracy. The fusion is performed using the following pixel level fusion algorithms: Principal Component Analysis (PCA), Averaging (Ave), Laplacian Pyramid, Filter Subtract Decimate (FSD), Ratio Pyramid, Contrast Pyramid, Gradient Pyramid, Discrete Wavelet Transform (QWT), Shift Invariant DWT (SIDWT) with Haar, Morphological Pyramid, and the recent image fusion method developed by AUG Signals Ltd. A MATLAB based dedicated image fusion toolbox, that includes several pixel level fusion, restoration and registration algorithms, has been recently developed by AUG Signals. This toolbox is used for the tradeoff studies.