Abstract
In recent years, the rapid development of remote sensing technology has made intelligent interpretation possible. However, remote sensing images have arbitrary object orientation, small object size and complex background compared with natural images, and these problems cause difficulty in accomplishing accurate object detection. In this study, several classical traditional object detection methods are first used to compare the detection effects based on the self-made high-resolution remote sensing image rotation detection dataset. Then, a dual-mode rotation regression network, namely, DRRN, is designed to solve the problem of arbitrary object orientation in remote sensing images. DRRN mainly consists of two parts: dual-mode region proposal network and rotation regression network. Meanwhile, a combined regression loss with intersection over union is proposed to improve the traditional smooth L1 loss. Next, a bi-directional cross-layer connected feature pyramid network, namely, bc-FPN, is proposed to solve the problem of small objects. And a supervised hybrid attention mechanism, namely, SHAM, is proposed to solve the problem of complex background, and the two modules are portable and plug-and-play. Experiments show that the proposed methods can effectively improve the detection effects of object in remote sensing images.
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Sun, T., Liu, K., Shi, J. (2023). Extraction Method of Rotated Objects from High-Resolution Remote Sensing Images. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14255. Springer, Cham. https://doi.org/10.1007/978-3-031-44210-0_24
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DOI: https://doi.org/10.1007/978-3-031-44210-0_24
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