A Counterfactual Fine-Grained Aircraft Classification Network for Remote Sensing Images Based on Normalized Coordinate Attention
Source abstract
Abstract
Fine-grained aircraft classification in remote sensing is a critical task within the field of remote sensing image processing, aiming to precisely distinguish between different types of aircraft in aerial images. Due to the high visual similarity among aircraft targets in remote sensing images, accurately capturing subtle and discriminative features becomes a key technical challenge for fine-grained aircraft classification. In this context, we propose a Normalized Coordinate Attention-Based Counterfactual Classification Network (NCC-Net), which emphasizes the spatial positional information of aircraft targets and effectively captures long-range dependencies, thereby enabling precise localization of various aircraft components. Furthermore, we analyze the proposed network from a causal perspective, encouraging the model to focus on key discriminative features of the aircraft while minimizing distraction from the surrounding environment and background. Experimental results on three benchmark datasets demonstrate the superiority of our method. Specifically, NCC-Net achieves Top-1 classification accuracies of 97.7% on FAIR1M, 95.2% on MTARSI2, and 98.4% on ARSI120, outperforming several state-of-the-art methods. These results highlight the effectiveness and generalizability of our proposed method for fine-grained remote sensing target recognition.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Remote Sensing and LiDAR Applications · Advanced Image Fusion Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Beijing Institute of Tracking and Communication Technology, Beijing 100094, China
School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
Funding
National Natural Science Foundation of China
References (46)
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