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19 July 2024 AFG-Net: a fine-grained classification network for aircrafts in remote sensing images based on improved ConvNext
Ruofei Ma, Jiefeng Xue, Ping Jiang, Gang Zhang, Tao Lei
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Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131811K (2024) https://doi.org/10.1117/12.3031113
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China
Abstract
In the context of the rapid development and widespread application of remote sensing technology, fine-grained aircraft classification is a research area with significant practical value. Most current classification algorithms, developed primarily for natural images, underperform when applied to remote sensing images. Additionally, fine-grained classification tasks inherently face challenges due to small inter-class differences and subtle discriminative features. To address these issues, this study designs an aircraft fine-grained network (AFG-Net) method, a network for fine-grained aircraft classification in remote sensing images. AFG-Net builds upon the ConvNext network, known for its superior classification capabilities over traditional CNNs and comparable to the Swin Transformer. Due to the influence of natural environment and complex imaging backgrounds on remote sensing images, this paper conducted data augmentation before training, which can help the network model better cope with interference and improve robustness. The following are the improvements made in this study: 1) Developing the ConvNext_s network, enhanced with the SimAm attention mechanism for better extraction of subtle, discriminative aircraft features. 2) Proposing a new composite loss function based on Mutual-Channel Loss, allowing the network to consider both global and local information more comprehensively, thereby improving aircraft classification performance and model robustness. 3) Demonstrating AFG-Net's applicability to fine-grained aircraft classification tasks in remote sensing images. Tested on the MTARSI and OPT-Aircraft_v1.0 datasets, AFG-Net achieves accuracies of 94.76% and 84.59%, respectively, outperforming existing advanced models in extensive experiments.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Ruofei Ma, Jiefeng Xue, Ping Jiang, Gang Zhang, and Tao Lei "AFG-Net: a fine-grained classification network for aircrafts in remote sensing images based on improved ConvNext", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131811K (19 July 2024); https://doi.org/10.1117/12.3031113
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KEYWORDS
Image classification
Remote sensing
Data modeling
Feature extraction
Education and training
Performance modeling
Network architectures
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