Unsupervised Dynamic Convolutional Neural Network Model for Hyperspectral and Multispectral Image Fusion

Abstract

In recent years, fusion methods based on unsupervised deep learning have achieved impressive performance in the fusion of hyperspectral image (HSI) and multispectral image (MSI). However, there are still some limitations in the current research. Most existing fusion methods only apply to simulated data and need more verification on real data sets. To solve these issues, this paper designed an unsupervised dynamic convolutional neural network fusion model (UDCNN), which can adaptively learn the radiometric difference between HSI and MSI. This model achieves better performance on simulated data compared with related unsupervised deep learning methods, and achieves more accurate results on real data through classification-oriented application of the fusion results.