Multi-sensor image fusion with the steered Hermite transform

Abstract

The steered Hermite Transform is presented as an efficient tool for multi-sensor image fusion. The fusion algorithm is based on the Hermite transform, which is an image representation model based on Gaussian derivatives that mimic some of the most important properties of human vision. Moreover, rotation of the Hermite coefficients allows efficient detection and reconstruction of oriented image patterns in reconstruction applications such as fusion and noise reduction. We show image fusion with different image sensors, namely synthetic aperture radar (SAR) and multispectral optical images. This case is important mainly because SAR sensors can obtain information independently of weather conditions; however, the characteristic noise (speckle) present in SAR images possesses serious limitations to the fusion process. Therefore noise reduction is a key point in the problem of image fusion. In our case, we combine fusion with speckle reduction in order to discriminate relevant information from noise in the SAR images. The local analysis properties of the Hermite transform help fusion and noise reduction adapt to the local image orientation and content. This is especially useful considering the multiplicative nature of speckle in SAR images.