@inproceedings{02a174cccf5b4a0eb5a61d3f5d5832c2,
title = "SA-YOLO: The Saliency Adjusted Deep Network for Optical Satellite Image Ship Detection",
abstract = "Ship detection from remote sensing images plays an important role in military and civilian fields. However, since the small size of ship targets and the interference of cloud cover, this task still suffers from great missed detection and false-alarm. To tackle these problems, a Saliency Adjusted YOLO (SA-YOLO) for optical satellite image ship detection is developed. First, due to the fact that the ship in low resolution imagery can be regarded as a salient object, we designed a saliency guided dense sampling layer (SDSL) to improve the spatial sampling of small ship targets. Secondly, the saliency region-aware convolution (SAConv) strategy is designed to improve the representation capability of salient regions and increase the attention of network to these regions. We validated the proposed method using more than 2000 remote sensing images from GF-1 satellite. The experimental results demonstrated that the proposed method obtained a better detection performance than the state-of-the-art methods.",
keywords = "Ship detection, convolutional neural network, deep learning, optical remote sensing images",
author = "Shuchen Wang and Hairan Sun and Yihang Zhu and Mingkai Li and Qizhi Xu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",
year = "2022",
doi = "10.1109/IGARSS46834.2022.9884682",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2131--2134",
booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",
address = "United States",
}