Few-Shot Learning With Mutual Information Enhancement for Hyperspectral Image Classification

In recent years, few-shot learning (FSL) has made significant progress in hyperspectral image classification (HSIC) by transferring metaknowledge from a labeled source domain to a target domain with very limited labeled samples. Considering that natural images have rich spatial texture information, heterogeneous FSL (HFSL) by using natural images as the source domain and hyperspectral image (HSI) as the target domain has shown excellent performance. However, some problems also exist in the HFSL, such as poor generalization ability from natural images to HSIs, prototype instability due to limited labeled samples, and domain shift between different types of images. To address these problems, we propose a mutual information enhancement FSL (MIEFSL) method for HSIC, which mainly contains three modules, i.e., mutual information enhancement (MIE), intradomain prototype rectification (IPR), and interdomain distribution alignment (IDA). In order to improve the generalization ability of the network and preserve the raw data information as much as possible, an MIE module is designed to maximize the mutual information (MI) between the support set samples and their corresponding masked samples. To stabilize the prototypes, an IPR module is constructed through a distribution expansion strategy. In addition, to alleviate domain shifts between different types of images, an IDA is performed between source and target domains. Experimental results demonstrate that the proposed MIEFSL outperforms existing state-of-the-art FSL methods and achieves the overall accuracy (OA) of 78.34%, 90.31%, and 91.72% on Indian Pines (IP), University of Pavia (UP), and Salinas (SA) in the case of only five labeled samples, respectively. © 1980-2012 IEEE.