Review of Zero-Shot Remote Sensing Image Scene Classification

Abstract

In recent years, remote sensing (RS) image scene classification methods have experienced notable development due to the powerful feature extraction ability of deep learning. However, current methods for RS image scene classification (RSSC) tasks struggle with handling unseen scene categories because of their reliance on large amounts of high-quality labeled data. To address this issue, zero-shot learning has been introduced into RSSC tasks, leading to the emergence of zero-shot remote sensing image scene classification (ZSRSSC) approaches. These methods refer to inference and recognition of unseen category images by comprehending seen category image features and combining category semantic information in a multimodal learning paradigm. The inference capability possessed by ZSRSSC methods can significantly improve the accuracy and applicability of RSSC in dealing with large amounts of data in the RS domain. However, this newly emerging field has produced numerous results without a comprehensive survey to organize research progress systematically. Therefore, this article systematically reviews the research progress made on these approaches, summarizes commonly used datasets and experimental setups, and compares the results of different methods. Specifically, the definition of zero-shot learning (ZSL) is reviewed first, and the mainstream development of ZSL classification methods is briefly introduced. Then, the definition of ZSRSSC is outlined, followed by an introduction to the prevalent ZSRSSC approaches and their categorization. Finally, the critical issues in these methods are analyzed, and their future development directions are discussed.

Keywords

• Multimodal learning
• remote sensing (RS) image scene classification
• zero-shot learning