Feature Consistency-Based Prototype Network for Open-Set Hyperspectral Image Classification

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 7; pp. 9286 - 9296
• Main Authors: Xie, Zhuojun, Duan, Puhong, Liu, Wang, Kang, Xudong, Wei, Xiaohui, Li, Shutao
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classification; Classification algorithms; Clustering; Clustering methods; Contrastive clustering; Contrastive learning; Convolutional neural networks; feature consistency; Feature extraction; hyperspectral image (HSI); Hyperspectral imaging; Image classification; Modules; open-set classification; prototype network; Prototypes; Testing; Training
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2022.3232225

Hyperspectral image (HSI) classification methods have made great progress in recent years. However, most of these methods are rooted in the closed-set assumption that the class distribution in the training and testing stages is consistent, which cannot handle the unknown class in open-world scenes. In this work, we propose a feature consistency-based prototype network (FCPN) for open-set HSI classification, which is composed of three steps. First, a three-layer convolutional network is designed to extract the discriminative features, where a contrastive clustering module is introduced to enhance the discrimination. Then, the extracted features are used to construct a scalable prototype set. Finally, a prototype-guided open-set module (POSM) is proposed to identify the known samples and unknown samples. Extensive experiments reveal that our method achieves remarkable classification performance over other state-of-the-art classification techniques.