Chinese Journal of Ship Research, Volume. 19, Issue 6, 275(2024)
Method of joint wavelet thresholding and F-NLM de-noising for high-resolution SAR ship detection
[4] [4] GU D, XU X. Multifeature extraction of ships from SAR images[C]Proceedings of the 6th International Congress on Image Signal Processing (CISP)Vol. 2 . Hangzhou, China: IEEE, 2013, 1: 454458.
[8] [8] ALAMPIDIS D, STEIN G W. Target detection based on multiresolution fractal analysis[C]Proceedings of Signal Processing, Sens Fusion, Target Recognition XVI. SPIE Vol. 6567. lo, Flida, USA: SPIE International Society f Optics Photonics, 2007: 512519.
[11] A BOUKERCHE, Z J HOU. Object detection using deep learning methods in traffic scenarios. ACM Computing Surveys (CSUR), 54, 1-35(2021).
[14] [14] BUADES A, COLL B, MEL JM. A nonlocal algithm f image denoising[C]Proceedings of 2005 IEEE Computer Society Conference on computer vision pattern recognition (CVPR''05)Vol. 2 . San Diego, CA, USA: IEEEE, 2005.
[20] [20] GENG X M, ZHAO L L, SHI L. Smallsized ship detection nearshe based on lightweight active learning model with a small number of labeled data f SAR imagery[J]. Remote Sensing, 2021, 13(17): 3400.
[25] [25] TAN M X, PANG R M, LE Q V. EfficientDet: scalable efficient object detection[C]Proceedings of the IEEECVF Conference on Computer Vision Pattern Recognition. Seattle, WA, USA: IEEE, 2020: 10778–1078.
Get Citation
Copy Citation Text
Liang TONG, Dan LIU, Zhongbo PENG, Han ZOU, Lumeng WANG, Chunyu ZHANG. Method of joint wavelet thresholding and F-NLM de-noising for high-resolution SAR ship detection[J]. Chinese Journal of Ship Research, 2024, 19(6): 275
Category: Weapon, Electronic and Information System
Received: Jul. 26, 2023
Accepted: --
Published Online: Mar. 14, 2025
The Author Email: