Fine-Grained Image Recognition Methods and Their Applications in Remote Sensing Images: A Review

Abstract

Fine-grained image recognition (FGIR), unlike traditional coarse-grained recognition, is centered on distinguishing fine-level subclasses within broader semantic categories. It holds significant scientific research value, particularly in remote sensing, where the precise identification of specific objects—such as ships, buildings, and land use categories—is critical for tasks like boundary security, environmental monitoring, and urban planning. Recent advancements in FGIR have notably improved feature representation and generalization, especially under the diverse imaging conditions typical of remote sensing. However, challenges remain, including the heavy reliance on high-quality large-scale fine-grained image data and difficulties in extracting subtle image features. Efficiently utilizing limited data and enhancing feature extraction capabilities have thus become key focus areas in current FGIR research. This article systematically reviews the advancements in FGIR, covering its foundational principles, key methodologies, and the latest research developments, while providing a comprehensive comparative analysis of their performance in remote sensing image applications. In addition, it addresses the specific challenges posed by fine-grained recognition in remote sensing imagery and explores potential directions for future research in this field.

Keywords

• Deep learning
• fine-grained image recognition (FGIR)
• local features
• remote sensing images