Abstract
Airplane fine-grained classification is a challenging task in the field of remote sensing, because it requires for differentiating varieties of airplanes. Convolutional neural networks (CNNs) have recently achieved remarkable progress, due to their ability to learn high-level feature representations. However, training CNNs requires a large number of data, and there are few mature and public data sets concerned on airplane fine-grained classification in remote sensing images. In this paper, we propose a transferred CNN-based model that focus on airplane fine-grained classification by adopting a pre-trained CNN-based model with a large source data set and fine-tuning the model on a small task-specific data set. For fine-tuning the model, we collect a new data set, 11 Types of Airplanes in Remote Sensing Images (ARSI-11), which consists of 2200 images of 11 types of airplanes. The experimental results demonstrate that our transferred CNN-based model significantly improves the classification performance than the comparative methods.
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This work is supported by the National Natural Science Foundation of China (Grant No. 61272317).
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Yan, L., Wan, S., Jin, P., Zou, C. (2018). Airplane Fine-Grained Classification in Remote Sensing Images via Transferred CNN-Based Models. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_34
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DOI: https://doi.org/10.1007/978-981-13-0893-2_34
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