Prototype Calibration with Feature Generation for Few-Shot Remote Sensing Image Scene Classification

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 14; p. 2728
• Main Authors: Zeng, Qingjie, Geng, Jie, Huang, Kai, Jiang, Wen, Guo, Jun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2021
• Subjects: Calibration; Classification; Coders; Deep learning; feature learning; few-shot learning; Image classification; Image enhancement; Influence; Neural networks; prototype calibration; Prototypes; Remote sensing; remote sensing classification; scene classification; Training
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13142728

Few-shot classification of remote sensing images has attracted attention due to its important applications in various fields. The major challenge in few-shot remote sensing image scene classification is that limited labeled samples can be utilized for training. This may lead to the deviation of prototype feature expression, and thus the classification performance will be impacted. To solve these issues, a prototype calibration with a feature-generating model is proposed for few-shot remote sensing image scene classification. In the proposed framework, a feature encoder with self-attention is developed to reduce the influence of irrelevant information. Then, the feature-generating module is utilized to expand the support set of the testing set based on prototypes of the training set, and prototype calibration is proposed to optimize features of support images that can enhance the representativeness of each category features. Experiments on NWPU-RESISC45 and WHU-RS19 datasets demonstrate that the proposed method can yield superior classification accuracies for few-shot remote sensing image scene classification.