  This paper presents a generative model method for multispectral image fusion
in remote sensing which is trained without supervision. This method eases the
supervision of learning and it also considers a multi-objective loss function
to achieve image fusion. The loss function incorporates both spectral and
spatial distortions. Two discriminators are designed to minimize the spectral
and spatial distortions of the generative output. Extensive experimentations
are conducted using three public domain datasets. The comparison results across
four reduced-resolution and three full-resolution objective metrics show the
superiority of the developed method over several recently developed methods.
