Multistage Relation Network With Dual-Metric for Few-Shot Hyperspectral Image Classification

• Published in: IEEE transactions on geoscience and remote sensing 2023-01, Vol.61, p.1-1
• Main Authors: Zeng, Jun, Xue, Zhaohui, Zhang, Ling, Lan, Qiuping, Zhang, Mengxue
• Format: Article
• Language: English
• Subjects: Classification; Convolution; deep learning (DL); Embedding; Feature extraction; few-shot learning (FSL); Hyperspectral image (HSI); Hyperspectral imaging; Image classification; Measurement; Methods; Modules; multistage structure; Recombination; relation network; Semisupervised learning; Silicon; Task analysis; Training
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3271424

Recently, few-shot learning (FSL) has exhibited great potentials in hyperspectral image (HSI) classification due to its promising performance under few training samples. Although existing FSL methods have achieved great success, some limitations can still be witnessed. On the one hand, current methods mainly rely on the single metric to identify, which cannot effectively represent the class distribution with few labeled samples. On the other hand, existing methods usually only use the last deep feature of feature extractor, which may lead to the under-utilization of scarce labeled samples. To overcome the above issues, a novel multistage relation network with dual-metric (DM-MRN) is proposed for few-shot HSI classification. Firstly, a sample recombination strategy is designed to increase the variety of classification tasks in training period. Secondly, an embedding module is employed to extract deep features of the input image patches. Thirdly, we propose two relation modules: image-to-class (I2C) block and image-to-image (I2I) block. I2C block is designed to compute I2C-level relation score between second-order features, and I2I block is conceived to generate I2I-level relation score between first-order features. Finally, DM-MRN is constructed by integrating one embedding module, two I2C blocks, and one I2I block. In addition, an adaptive weighting strategy is designed to fuse the obtained relation scores, and classification can be achieved by assigning each query sample to the class with the highest value of the fused relation score. Extensive experiments carried out on five popular HSI data sets demonstrate that the proposed method outperforms other traditional and advanced models under few training samples in terms of classification accuracy and generalization performance, i.e., the performance improvement in terms of OA is around 0.30%-27.98% under 10 labeled samples per class.