Improved prototypical networks for remote sensing scene classification

Yujin Zhou; Xiang Zhang; Jie Li; Guoqing Wang; Yizhuang Xie

doi:10.1117/12.2681055

8 June 2023 Improved prototypical networks for remote sensing scene classification

Yujin Zhou, Xiang Zhang, Jie Li, Guoqing Wang, Yizhuang Xie

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127072E (2023) https://doi.org/10.1117/12.2681055
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

In practical applications, remote sensing (RS) scene classification faces data shift problems, including novel class and data discrepancy problems. Due to these problems, it is difficult to obtain representative and discriminative features. Therefore, we propose improved prototypical networks (IPN) based on few-shot learning to solve data shift problems in RS scene classification. First, a novel and effective scheme is proposed, which is to introduce Vision Transformer (ViT) pre-trained on a large-scale dataset as the feature extractor of the prototypical networks. Based on the meta-task training framework, IPN can adapt well to RS scene classification tasks and obtain representative features. In addition, a novel loss function called self-distillation-based prototype loss is designed to obtain discriminative features by introducing inter-sample selfdistillation and inter-layer self-distillation methods. Extensive experiments are conducted on several public RS scene datasets. Compared with the existing methods, the proposed method achieves an improvement of 4.18%-21.42%. Results demonstrate that the proposed method can effectively solve the data shift problems in RS scene classification.

Citation Download Citation

Yujin Zhou, Xiang Zhang, Jie Li, Guoqing Wang, and Yizhuang Xie "Improved prototypical networks for remote sensing scene classification", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127072E (8 June 2023); https://doi.org/10.1117/12.2681055

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KEYWORDS

Remote sensing

Prototyping

Scene classification

Feature extraction

Education and training

Feature fusion

Transformers

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