Improving Few-Shot Remote Sensing Scene Classification With Class Name Semantics

Abstract

Few-shot remote sensing scene classification (FSRSSC) has been used for new class recognition in the presence of a limited number of labeled samples. The representation vector (prototype) of categories obtained using images only confronts some challenges, such as insufficient generalization when the number of samples is too small. To address this problem, we propose a new FSRSSC method based on prototype networks, named CNSPN, which combines semantic information of class names (name of the scene categories, such as aircraft, harbor, and bridge). First, CNSPN extracts semantics for class names using a pre-trained word-embedding model, which enriches the feature representation ability of the category at the source. Then, an enhanced fusion prototype is generated by fusing the semantic information of text and visual information in the image through a multimodal prototype fusion module (MPFM). Finally, the query image is classified by measuring the distance between the query sample and the visual prototype, and between the query sample and the fusion prototype. Comparative experiments on the NWPU-RESISC45 and RSD46-WHU datasets show that the proposed method significantly improves FSRSSC performance. Code is available at https://github.com/RS-CSU/CNSPN.git.