This paper presents a generative model method for multispectral image fusion in remote sensing which involves training without supervision. This method eases the supervision of learning and also uses a multi-objective loss function to achieve image fusion. The loss function used 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.

本文提出了一种用于遥感多光谱图像融合的生成模型方法，该方法涉及无监督训练。这种方法减轻了学习的监督，还使用了多目标损失函数来实现图像融合。使用的损失函数结合了光谱和空间失真。设计了两个鉴别器来最小化生成输出的光谱和空间失真。使用三个公共领域数据集进行了广泛的实验。四个降低分辨率和三个全分辨率客观指标的比较结果表明，所开发的方法优于最近开发的几种方法。