Pansharpening enhances spatial details of high spectral resolution multispectral images using features of high spatial resolution panchromatic image. There are a number of traditional pansharpening approaches but producing an image exhibiting high spectral and spatial fidelity is still an open problem. Recently, deep learning has been used to produce promising pansharpened images; however, most of these approaches apply similar treatment to both multispectral and panchromatic images by using the same network for feature extraction. In this work, we present present a novel dual attention-based two-stream network. It starts with feature extraction using two separate networks for both images, an encoder with attention mechanism to recalibrate the extracted features. This is followed by fusion of the features forming a compact representation fed into an image reconstruction network to produce a pansharpened image. The experimental results on the Pl\'{e}iades dataset using standard quantitative evaluation metrics and visual inspection demonstrates that the proposed approach performs better than other approaches in terms of pansharpened image quality.
翻译:Pansharping利用高空间分辨率全色图像的特征,加强了高光谱分辨率多光谱图像的空间细节。有一些传统的全色方法,但生成显示高光谱和空间忠诚的图像仍然是一个尚未解决的问题。最近,利用深层学习产生了充满希望的全光图像;然而,大多数这些方法对多光谱和全色图像都采用类似的处理方法,使用同一网络进行地貌提取。在这项工作中,我们展示了一个新的双向关注双流网络。从地貌提取开始,使用两种不同的网络,即一个关注机制对提取的图像进行重新校正的编码器。随后,将一些特征组成一个缩压式代表器,纳入一个图像重建网络,以生成一个全光图像。Pl\'{e}iades数据集的实验结果显示,使用标准的定量评价指标和直观检查,在光谱图像质量方面,拟议的方法比其他方法表现得更好。