Abstract
Deep learning-based ship detection from SAR data is one of the challenging problems in the remote sensing area. Also, SAR ship detection is precise object detection and pattern recognition task under the computer vision area. The main problems are false detection, primarily due to speckle presence and multi-scale SAR image availability. We propose a novel real-time system with a preprocessing technique exclusively for SAR ship detection to address this problem. The proposed SarNeDe preprocessing stage is specially designed using image processing techniques and lee filter to reduce the false prediction and improve the SAR image quality, which increases the detection accuracy because the lee filter alone could increase missed detections. The SarNeDe image is generated from raw SAR image and is given to a novel multi-scale lightweight deep learning model to predict all ships’ positions. The proposed model has a feature merging & boosting network and three detection parts for detecting big, medium, & small ships. We experimented on the public SAR ship detection dataset (SSDD) and Dataset of Ship Detection for Deep Learning under Complex Backgrounds (SDCD) to validate the proposed method’s feasibility. The experimental results indicated that our proposed method’s ship detection accuracy is superior to other state-of-the-art ship detectors with reduced false detections.
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Alswayed AS, Alhichri HS, Bazi Y (2020) Squeezenet with attention for remote sensing scene classification. In: 2020 3Rd international conference on computer applications & information security (ICCAIS), IEEE, pp 1–4
Bao W, Huang M, Zhang Y, Xu Y, Liu X, Xiang X (2021) Boosting ship detection in sar images with complementary pretraining techniques. arXiv:2103.08251
Chang YL, Anagaw A, Chang L, Wang YC, Hsiao CY, Lee WH (2019) Ship detection based on yolov2 for sar imagery. Remote Sens 11(7):786
Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258
Collobert R, Weston J (2008) A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th international conference on Machine learning, pp 160–167
Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448
Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587
Hassaballah M, Awad AI (2016) Detection and description of image features: an introduction. In: Image feature detectors and descriptors, Springer, pp 1–8
Hassaballah M, Awad AI (2020) Deep learning in computer vision: principles and applications. CRC Press
Hassaballah M, Hosny KM (2019) Recent advances in computer vision. Studies in Computational Intelligence, 804
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861
Lee JS (1981) Speckle analysis and smoothing of synthetic aperture radar images. Computer Graphics and Image Processing 17(1):24–32
Li D, Liang Q, Liu H, Liu Q, Liu H, Liao G (2021) A novel multidimensional domain deep learning network for sar ship detection. IEEE Trans Geosci Remote Sens, pp 1–13. https://doi.org/10.1109/TGRS.2021.3062038
Li J, Qu C, Shao J (2017) Ship detection in sar images based on an improved faster r-cnn. In: 2017 SAR In big data era: models, methods and applications (BIGSARDATA), IEEE, pp 1–6
Li J, Qu C, Shao J (2017) Ship detection in sar images based on an improved faster r-cnn. In: 2017 SAR In big data era: models, methods and applications (BIGSARDATA), IEEE, pp 1–6
Lin Z, Ji K, Leng X, Kuang G (2018) Squeeze and excitation rank faster r-cnn for ship detection in sar images. IEEE Geosci Remote Sens Lett 16 (5):751–755
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In: European conference on computer vision, Springer, pp 21–37
Mao Y, Yang Y, Ma Z, Li M, Su H, Zhang J (2020) Efficient low-cost ship detection for sar imagery based on simplified u-net. IEEE Access 8:69,742–69,753
M.Helal H., I.Hassan O. (2017) Maritime surveillance: an integral part of maritime security. BORDER SECURITY AND SAFETY 184
Van den Oord A, Dieleman S, Schrauwen B (2013) Deep content-based music recommendation. In: Advances in neural information processing systems, pp 2643–2651
Park S, Jeong Y, Kim HS (2017) Multiresolution cnn for reverberant speech recognition. In: 2017 20Th conference of the oriental chapter of the international coordinating committee on speech databases and speech i/o systems and assessment (o-COCOSDA), IEEE, pp 1–4
Raj JA, Idicula SM, Paul B (2019) Sar target identification using sar-com technique. In: 2019 9Th international conference on advances in computing and communication (ICACC), IEEE, pp 92–96
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788
Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263–7271
Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. CoRR arXiv:abs/1804.02767
Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99
Tsantekidis A, Passalis N, Tefas A, Kanniainen J, Gabbouj M, Iosifidis A (2017) Forecasting stock prices from the limit order book using convolutional neural networks. In: 2017 IEEE 19Th conference on business informatics (CBI), vol 1, IEEE, pp 7–12
Wang Y, Wang C, Zhang H, Dong Y, Wei S (2019) Automatic ship detection based on retinanet using multi-resolution gaofen-3 imagery. Remote Sens 11(5):531
Wang Y, Wang C, Zhang H, Dong Y, Wei S (2019) A sar dataset of ship detection for deep learning under complex backgrounds. Remote Sens 11 (7):765
Zhang T, Zhang X (2019) High-speed ship detection in sar images based on a grid convolutional neural network. Remote Sens 11(10):1206
Zhang T, Zhang X (2020) Shipdenet-20: An only 20 convolution layers and< 1-mb lightweight sar ship detector. IEEE Geoscience and Remote Sensing Letters
Zhang T, Zhang X, Shi J, Wei S (2019) Depthwise separable convolution neural network for high-speed sar ship detection. Remote Sens 11(21):2483
Zhao J, Guo W, Zhang Z, Yu W (2019) A coupled convolutional neural network for small and densely clustered ship detection in sar images. Sci China Inform Sci 62(4):42,301
Zhao Y, Zhao L, Xiong B, Kuang G (2020) Attention receptive pyramid network for ship detection in sar images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:2738–2756
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J, A.R., Idicula, S.M. & Paul, B. A novel sarnede method for real-time ship detection from synthetic aperture radar image. Multimed Tools Appl 81, 16921–16944 (2022). https://doi.org/10.1007/s11042-022-12243-1
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DOI: https://doi.org/10.1007/s11042-022-12243-1