Heterogeneous Data-based Cross-domain Few-shot Classification Method of Hyperspectral Image

Abstract

Few-shot learning (FSL) has been employed in hyperspectral image (HSI) classification, achieving excellent performance with limited training data. However, existing HSI few-shot classification methods often encounter the problem of insufficient domain-transferable knowledge learning that is either from natural images or HSI solely. In this paper, we propose a two-stage cross-domain few-shot classification method of HSI, which for the first time makes use of heterogeneous labeled natural images and HSIs in the source domain (SD) to support the classification of novel classes in the target HSI domain. We first use a large amount of labeled natural images at the first stage to pre-train a backbone, which will be used to extract the spatial feature of HSIs at the second stage with fine-tuning. In the second stage, we propose a cross-domain few-shot classification method, which allows for effective discriminative feature learning in the target HSI domain with the transferred knowledge of the old classes obtained from natural images and HSIs in the source domain. To obtain domain-transferable knowledge, FSL is employed on the HSI source and target domain. To deal with the domain shift problem, we propose a class-matching based cross-domain contrastive loss. In addition, we take into account the large spectral variations problem in the target HSI domain and introduce an instance-level self-supervised loss. Experimental results on real data sets demonstrate that our method outperforms the recent state-of-the-art.