Fine-Grained Interpretation of Remote Sensing Image: A Review

This article conducts a systematic review on the fine-grained interpretation of remote sensing images, delving deeply into its background, current situation, datasets, methodology, and future trends, aiming to provide a comprehensive reference framework for research in this field. In terms of fine-grained interpretation datasets, with a focus on introducing representative datasets, and analyze their key characteristics such as the number of categories, sample size, and resolution, as well as their benchmarking role in research. For methodologies, by classifying the core methods according to the interpretation level system, this paper systematically summarizes the methods, models, and architectures for implementing fine-grained remote sensing image interpretation based on deep learning at different levels such as pixel-level classification and segmentation, object-level detection, and scene-level recognition. Finally, we summarize the challenges currently faced by the research (such as the distinction of highly similar categories, cross-sensor domain migration, and high annotation costs), and look forward to future directions, emphasizing the need to enhance the generalization, support open-world recognition further, and adapt to actual complex scenarios, etc. This review aims to promote the application of fine-grained interpretation technology for remote sensing images across a broader range of fields.