Citation: | ZHOU Xueke, LIU Chang, and ZHOU Bin. Ship detection in SAR images based on multi-scale features fusion and channel relation calibration of features[J]. Journal of Radars, 2021, 10(4): 531–543. doi: 10.12000/JR21021 |
[1] |
MOREIRA A, PRATS-IRAOLA P, YOUNIS M, et al. A tutorial on synthetic aperture radar[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(1): 6–43. doi: 10.1109/MGRS.2013.2248301
|
[2] |
郭倩, 王海鹏, 徐丰. SAR图像飞机目标检测识别进展[J]. 雷达学报, 2020, 9(3): 497–513. doi: 10.12000/JR20020
GUO Qian, WANG Haipeng, and XU Feng. Research progress on aircraft detection and recognition in SAR imagery[J]. Journal of Radars, 2020, 9(3): 497–513. doi: 10.12000/JR20020
|
[3] |
WACKERMAN C C, FRIEDMAN K S, PICHEL W G, et al. Automatic detection of ships in RADARSAT-1 SAR imagery[J]. Canadian Journal of Remote Sensing, 2001, 27(5): 568–577. doi: 10.1080/07038992.2001.10854896
|
[4] |
陈慧元, 刘泽宇, 郭炜炜, 等. 基于级联卷积神经网络的大场景遥感图像舰船目标快速检测方法[J]. 雷达学报, 2019, 8(3): 413–424. doi: 10.12000/JR19041
CHEN Huiyuan, LIU Zeyu, GUO Weiwei, et al. Fast detection of ship targets for large-scale remote sensing image based on a cascade convolutional neural network[J]. Journal of Radars, 2019, 8(3): 413–424. doi: 10.12000/JR19041
|
[5] |
李健伟, 曲长文, 邵嘉琦, 等. 基于深度学习的SAR图像舰船检测数据集及性能分析[C]. 第五届高分辨率对地观测学术年会论文集, 西安, 2018: 180–201.
LI Jianwei, QU Changwen, SHAO Jiaqi, et al. Dataset and performance analysis of ship detection methods based on deep learning[C]. The 5th China High Resolution Earth Observation Conference, Xi’an, China, 2018: 180–201.
|
[6] |
GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 580–587. doi: 10.1109/CVPR.2014.81.
|
[7] |
GIRSHICK R. Fast R-CNN[C]. IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015: 1440–1448. doi: 10.1109/ICCV.2015.169.
|
[8] |
REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149. doi: 10.1109/TPAMI.2016.2577031
|
[9] |
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016: 779–788. doi: 10.1109/CVPR.2016.91.
|
[10] |
LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector[C]. The 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 21–37. doi: 10.1007/978-3-319-46448-0_2.
|
[11] |
李健伟, 曲长文, 彭书娟. 基于级联CNN的SAR图像舰船目标检测算法[J]. 控制与决策, 2019, 34(10): 2191–2197. doi: 10.13195/j.kzyjc.2018.0168
LI Jianwei, QU Changwen, and PENG Shujuan. A ship detection method based on cascade CNN in SAR images[J]. Control and Decision, 2019, 34(10): 2191–2197. doi: 10.13195/j.kzyjc.2018.0168
|
[12] |
李广帅, 苏娟, 李义红. 基于改进Faster R-CNN的SAR图像飞机检测算法[J]. 北京航空航天大学学报, 2021, 47(1): 159–168. doi: 10.13700/j.bh.1001-5965.2020.0004
LI Guangshuai, SU Juan, and LI Yihong. An aircraft detection algorithm in SAR image based on improved Faster R-CNN[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(1): 159–168. doi: 10.13700/j.bh.1001-5965.2020.0004
|
[13] |
WANG Rui, SHAO Sihan, AN Mengyu, et al. Soft thresholding attention network for adaptive feature denoising in SAR ship detection[J]. IEEE Access, 2021, 9: 29090–29105. doi: 10.1109/ACCESS.2021.3059033
|
[14] |
ZHANG Tianwen and ZHANG Xiaoling. High-speed ship detection in SAR images based on a grid convolutional neural network[J]. Remote Sensing, 2019, 11(10): 1206. doi: 10.3390/rs11101206
|
[15] |
ZHANG Tianwen, ZHANG Xiaoling, SHI Jun, et al. Depthwise separable convolution neural network for high-speed SAR ship detection[J]. Remote Sensing, 2019, 11(21): 2483. doi: 10.3390/rs11212483
|
[16] |
ZHANG Tianwen and ZHANG Xiaoling. ShipDeNet-20: An only 20 convolution layers and <1-MB lightweight SAR ship detector[J]. IEEE Geoscience and Remote Sensing Letters, in press.
|
[17] |
ZHANG Tianwen, ZHANG Xiaoling, SHI Jun, et al. HyperLi-Net: A hyper-light deep learning network for high-accurate and high-speed ship detection from synthetic aperture radar imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 167: 123–153. doi: 10.1016/j.isprsjprs.2020.05.016
|
[18] |
GHIASI G, LIN T Y, and LE Q V. NAS-FPN: Learning scalable feature pyramid architecture for object detection[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019: 7029–7038. doi: 10.1109/CVPR.2019.00720.
|
[19] |
LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2017: 936–944, doi: 10.1109/CVPR.2017.106.
|
[20] |
HE Kaiming, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017: 2980–2988. doi: 10.1109/ICCV.2017.322.
|
[21] |
BODLA N, SINGH B, CHELLAPPA R, et al. Soft-NMS—Improving object detection with one line of code[C]. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017: 5562–5570. doi: 10.1109/ICCV.2017.593.
|
[22] |
HU Jie, LI Shen, GANG Sun, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011–2023. doi: 10.1109/TPAMI.2019.2913372
|
[23] |
ZHANG Tianwen, ZHANG Xiaoling, KE Xiao, et al. LS-SSDD-v1.0: A deep learning dataset dedicated to small ship detection from large-scale sentinel-1 SAR images[J]. Remote Sensing, 2020, 12(18): 2997. doi: 10.3390/rs12182997
|
[24] |
WEI Shunjun, ZENG Xiangfeng, QU Qizhe, et al. HRSID: A high-resolution SAR images dataset for ship detection and instance segmentation[J]. IEEE Access, 2020, 8: 120234–120254. doi: 10.1109/ACCESS.2020.3005861
|
[25] |
孙显, 王智睿, 孙元睿, 等. AIR-SARShip-1.0: 高分辨率SAR舰船检测数据集[J]. 雷达学报, 2019, 8(6): 852–862. doi: 10.12000/JR19097
SUN Xian, WANG Zhirui, SUN Yuanrui, et al. AIR-SARShip-1.0: High-resolution SAR ship detection dataset[J]. Journal of Radars, 2019, 8(6): 852–862. doi: 10.12000/JR19097
|
[26] |
CAI Zhaowei and VASCONCELOS N. Cascade R-CNN: Delving into high quality object detection[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 6154–6162, doi: 10.1109/CVPR.2018.00644.
|
[27] |
LIU Shu, QI Lu, QIN Haifang, et al. Path aggregation network for instance segmentation[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 8759–8768. doi: 10.1109/CVPR.2018.00913.
|
[28] |
ZHANG Tianwen, ZHANG Xiaoling, SHI Jun, et al. Balance scene learning mechanism for offshore and inshore ship detection in SAR images[J]. IEEE Geoscience and Remote Sensing Letters, in press.
|