A Novel Unified Framework for Multi-Task Fusion of Hyperspectral and SAR Image
Yifan Zhang, Yang Xu, Zebin Wu, Zhihui Wei, Jocelyn Chanussot · 2023
Classification using multi-source remote sensing data has attracted widespread attention and is playing an increasingly important role in various fields. However, due to the disparity in imaging methods and the informational imbalance among data from various sources, it is still challenging to incorporate complementary advantages. Furthermore, real-world situations frequently involve varied data resolution, and the outcomes of straightforward super-resolution preprocessing are not always helpful for performing subsequent classification tasks. In this paper, we propose a unified framework for joint super-resolution and classification tasks of low resolution hyperspectral images (LR-HSIs) and SAR images. The minimization of the proposed joint loss function in the generative adversarial network (GAN) framework, including super-resolution and classification objective functions, can effectively achieve the multi-task goals. Experimental results on the super-resolution and classification of real LR-HSIs and SAR images demonstrate the effectiveness of the proposed method both visually and quantitatively.