An Unsupervised Model Based on Convolutional Neural Network for Fusing Landsat-8 and Sentinel-2 Data

Medium spatial resolution satellite image series is important for some applications. Currently, the most widely used medium resolution data is from Sentinel-2 and Landsat-8 satellites. For the purpose of acquiring dense image series, the fusion of them has drawn much attention. In this field, the deep learning-based fusion models have appeared excellent performance, which demonstrated the applicability for deep learning for the fusion. However, existing deep learning based fusion models all need high quality and abundant training data, which may limit their applications. To address this problem, we develop an unsupervised fusion model based on convolutional neural networks (CNNs), which can fuse the Sentinel-2 and Landsat images without other training data. This fusion model takes full advantage of CNNs in the image fusion task, but get raid of the limitation of the requirement of training data. In the experiment, we test the new model using the Sentinel-Landsat dataset, and compare it with other two fusion approaches. The experimental results indicate that the proposed fusion model has remarkable fusion performance especially in terms of accuracy. However, it is can not capture the surface changes well, which is the main direction for us to improve it in the future.