SAR (Synthetic Aperture Radar) Image Study and Analysis for Object Recognition in Surveillance

Authors

  • Sushant J. Pawar Dept of E&TC Pune Institute of Computer Technology, Pune, Dept of E&TC Sandip Institute of Technology & Research Centre Nashik
  • Sanjay. T. Gandhe Professor, Pune Institute of Computer Technology, Pune

Keywords:

detection, feature extraction, classification, ship type, SAR images

Abstract

A summary of the many techniques used for this comprehensive review study aims to classify and detect synthetic aperture radar (SAR) images. SAR images have become more well-liked as a result of their adaptability and use in remote sensing activities such as planning, surveillance, and search and rescue regardless of the weather. The conversion of radar scatter returns to images and subsequent analysis for composition determination make it difficult to interpret these images efficiently. SAR images have been effectively categorized in the past for a variety of uses, with the possibility for further expansion across other SAR image types. In particular, feature extraction and SAR image categorization using Convolutional Neural Networks (CNNs) show potential.

Downloads

Download data is not yet available.

References

A. Moreira et al., “Tandem-L: A Highly Innovative Bistatic SAR Mission for Global Observation of Dynamic Processes on the Earth’s Surface,” IEEE Geosci. Remote Sens. Mag., vol. 3, no. 2, pp. 8–23, 2015, doi: 10.1109/MGRS.2015.2437353.

K. Ouchi, “Current Status on Vessel Detection and Classification by Synthetic Aperture Radar for Maritime Security and Safety,” 38th Symp. Remote Sens. Environ. Sci., no. September, pp. 5–12, 2016, Accessed: Apr. 20, 2023. [Online]. Available: https://www.researchgate.net/profile/Kazuo-Ouchi-2/publication/308917393_Current_Status_on_Vessel_Detection_and_Classification_by_Synthetic_Aperture_Radar_for_Maritime_Security_and_Safety/links/5d11dd51299bf1547c7c7fc3/Current-Status-on-Vessel-Detection-a

M. Ma, J. Chen, W. Liu, and W. Yang, “Ship classification and detection based on CNN using GF-3 SAR images,” Remote Sens., vol. 10, no. 12, pp. 1–22, 2018, doi: 10.3390/rs10122043.

H. Zhang, X. Tian, C. Wang, F. Wu, and B. Zhang, “Merchant vessel classification based on scattering component analysis for COSMO-SkyMed SAR images,” IEEE Geosci. Remote Sens. Lett., vol. 10, no. 6, pp. 1275–1279, 2013, doi: 10.1109/LGRS.2012.2237377.

H. Lang, S. Wu, and Y. Xu, “Ship Classification in SAR Images Improved by AIS Knowledge Transfer,” IEEE Geosci. Remote Sens. Lett., vol. 15, no. 3, pp. 439–443, 2018, doi: 10.1109/LGRS.2018.2792683.

H. Lin, S. Song, J. Y.-R. Sensing, and undefined 2018, “Ship classification based on MSHOG feature and task-driven dictionary learning with structured incoherent constraints in SAR images,” mdpi.com, doi: 10.3390/rs10020190.

U. Srinivas, V. Monga, and R. G. Raj, “Meta-classifiers for exploiting feature dependencies in automatic target recognition,” in IEEE National Radar Conference - Proceedings, 2011, pp. 147–151. doi: 10.1109/RADAR.2011.5960517.

A. Sami and M. E. Abdulmunem, “Synthetic aperture radar image classification: A survey,” Iraqi J. Sci., vol. 61, no. 5, pp. 1223–1232, 2020, doi: 10.24996/ijs.2020.61.5.29.

P. Mastro, G. Masiello, C. Serio, and A. Pepe, “Change Detection Techniques with Synthetic Aperture Radar Images: Experiments with Random Forests and Sentinel-1 Observations,” Remote Sens., vol. 14, no. 14, 2022, doi: 10.3390/rs14143323.

R. Lanari, M. Tesauro, E. Sansosti, and G. Fornaro, “Spotlight SAR data focusing based on a two-step processing approach,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 9, pp. 1993–2004, 2001, doi: 10.1109/36.951090.

C. Oliver and S. Quegan, Understanding synthetic aperture radar images. 2004. Accessed: Apr. 07, 2023. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=IeGKe40S77AC&oi=fnd&pg=PR17&dq=Oliver,+C.%3B+Quegan,+S.+Understanding+Synthetic+Aperture+Radar+Images&ots=3eJXo5avbD&sig=qKBj5Fx15sKEUE4w-m9thdEbdIA

X. Li and J. Chong, “Processing of envisat alternating polarization data for vessel detection,” IEEE Geosci. Remote Sens. Lett., vol. 5, no. 2, pp. 271–275, 2008, doi: 10.1109/LGRS.2008.915933.

C. Wang, F. Bi, W. Zhang, and L. Chen, “An Intensity-Space Domain CFAR Method for Ship Detection in HR SAR Images,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 4, pp. 529–533, 2017, doi: 10.1109/LGRS.2017.2654450.

H. Shi et al., “A novel ship detection method based on gradient and integral feature for single-polarization synthetic aperture radar imagery,” Sensors (Switzerland), vol. 18, no. 2, 2018, doi: 10.3390/s18020563.

J. Jiao et al., “A densely connected end-to-end neural network for multiscale and multiscene SAR ship detection,” IEEE Access, vol. 6, pp. 20881–20892, 2018, doi: 10.1109/ACCESS.2018.2825376.

B. J. Dange et al., “Grape Vision: A CNN-Based System for Yield Component Analysis of Grape Clusters,” ijisae.orgBJ Dange, PK Mishra, KV Metre, S Gore, SL Kurkute, HE Khodke, S GoreInternational J. Intell. Syst. Appl. Eng. 2023•ijisae.org, vol. 2023, no. 9s, pp. 239–244, Accessed: Aug. 07, 2023. [Online]. Available: https://www.ijisae.org/index.php/IJISAE/article/view/3113

E. E. Kuruoǧlu and J. Zerubia, “Modeling SAR images with a generalization of the Rayleigh distribution,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 527–533, 2004, doi: 10.1109/TIP.2003.818017.

H. C. Li, W. Hong, Y. R. Wu, and P. Z. Fan, “An efficient and flexible statistical model based on generalized gamma distribution for amplitude sar images,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 6, pp. 2711–2722, 2010, doi: 10.1109/TGRS.2010.2041239.

M. Tello, C. López-Martínez, and J. J. Mallorqui, “A novel algorithm for ship detection in SAR imagery based on the wavelet transform,” IEEE Geosci. Remote Sens. Lett., vol. 2, no. 2, pp. 201–205, 2005, doi: 10.1109/LGRS.2005.845033.

H. Yonggang and W. Yi, “Number estimation of small-sized ships in remote sensing image based on cumulative projection curve,” in ICALIP 2008 - 2008 International Conference on Audio, Language and Image Processing, Proceedings, 2008, pp. 1522–1526. doi: 10.1109/ICALIP.2008.4590172.

Z. Hong, Q. Jiang, H. Guan, and F. Weng, “Measuring overlap-rate in hierarchical cluster merging for image segmentation and ship detection,” in Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, 2007, pp. 420–424. doi: 10.1109/FSKD.2007.392.

S. He, S. Yang, A. Shi, and T. Li, “A novel image moving sea targets detection method based on natural measure feature,” in 2008 International Symposium on Information Science and Engineering, ISISE 2008, 2008, pp. 397–400. doi: 10.1109/ISISE.2008.49.

M. Tello, C. Lopez-Martinez, J. J. Mallorqui, and R. Bonastre, “Automatic detection of spots and extraction of frontiers in SAR images by means of the wavelet transform: Application to ship and coastline detection,” in International Geoscience and Remote Sensing Symposium (IGARSS), 2006, pp. 383–386. doi: 10.1109/IGARSS.2006.103.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–1149, 2017, doi: 10.1109/TPAMI.2016.2577031.

K. J. Ngeno, “H-039 Ship Classification Using Faster Region Convolution Neural Network ( Faster R-CNN ) for Automatic Identification of Marine vessels,” ieice.org, pp. 275–276, 2020, Accessed: Apr. 20, 2023. [Online]. Available: https://www.ieice.org/publications/conference-FIT-DVDs/FIT2017/data/pdf/H-039.pdf

C. Dao-Duc, H. Xiaohui, and O. Morère, “Maritime vessel images classification using deep convolutional neural networks,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Dec. 2015, pp. 276–281. doi: 10.1145/2833258.2833266.

S. Gore et al., “Innovations in Smart City Water Supply Systems,” ijisae.orgS Gore, I Dutt, RP Dahake, HE Khodke, SL Kurkute, BJ Dange, S GoreInternational J. Intell. Syst. Appl. Eng. 2023•ijisae.org, vol. 2023, no. 9s, pp. 277–281, Accessed: Aug. 07, 2023. [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/3118

S. Huang, H. Xu, and X. Xia, “Active deep belief networks for ship recognition based on BvSB,” Optik (Stuttg)., vol. 127, no. 24, pp. 11688–11697, 2016, doi: 10.1016/j.ijleo.2016.09.089.

J. He, Y. Wang, H. Liu, and N. Wang, “PolSAR Ship Detection Using Local Scattering Mechanism Difference Based on Regression Kernel,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 10, pp. 1725–1729, 2017, doi: 10.1109/LGRS.2017.2731049.

K. Fu, J. Fu, Z. Wang, and X. Sun, “Scattering-Keypoint-Guided Network for Oriented Ship Detection in High-Resolution and Large-Scale SAR Images,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 11162–11178, 2021, doi: 10.1109/JSTARS.2021.3109469.

Y. Zhou, F. Zhang, F. Ma, D. Xiang, and F. Zhang, “Small Vessel Detection Based on Adaptive Dual-Polarimetric Feature Fusion and Sea-Land Segmentation in SAR Images,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 15, pp. 2519–2534, 2022, doi: 10.1109/JSTARS.2022.3158807.

P. Chen, H. Zhou, Y. Li, B. Liu, and P. Liu, “Shape Similarity Intersection-Over-Union Loss Hybrid Model for Detection of Synthetic Aperture Radar Small Ship Objects in Complex Scenes,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 9518–9529, 2021, doi: 10.1109/JSTARS.2021.3112469.

F. Gao, W. Shi, J. Wang, E. Yang, and H. Zhou, “Enhanced feature extraction for ship detection from multi-resolution and multi-scene synthetic aperture radar (SAR) images,” Remote Sens., vol. 11, no. 22, 2019, doi: 10.3390/rs11222694.

L. Tang, W. Tang, X. Qu, Y. Han, W. Wang, and B. Zhao, “A Scale-Aware Pyramid Network for Multi-Scale Object Detection in SAR Images,” Remote Sens., vol. 14, no. 4, pp. 1–24, 2022, doi: 10.3390/rs14040973.

D. Li, S. Member, Q. Liang, H. Liu, and S. Member, “A Novel Multidimensional Domain Deep Learning Network for SAR Ship Detection,” vol. 60, pp. 1–13, 2021.

Q. An, Z. Pan, and H. You, “Ship detection in gaofen-3 SAR images based on sea clutter distribution analysis and deep convolutional neural network,” Sensors (Switzerland), vol. 18, no. 2, 2018, doi: 10.3390/s18020334.

H. Lang, Y. Xi, and X. Zhang, “Ship Detection in High-Resolution SAR Images by Clustering Spatially Enhanced Pixel Descriptor,” IEEE Trans. Geosci. Remote Sens., vol. 57, no. 8, pp. 5407–5423, 2019, doi: 10.1109/TGRS.2019.2899337.

J. R. A. Morillas, I. C. García, and U. Zölzer, “Ship detection based on SVM using color and texture features,” Proc. - 2015 IEEE 11th Int. Conf. Intell. Comput. Commun. Process. ICCP 2015, pp. 343–350, 2015, doi: 10.1109/ICCP.2015.7312682.

A. O. Hashi, I. H. Hussein, O. E. R. Rodriguez, A. A. Abdirahman, and M. A. Elmi, “Ship Detection Approach Using Machine Learning Algorithms,” Lect. Notes Data Eng. Commun. Technol., vol. 127, no. August, pp. 16–25, 2022, doi: 10.1007/978-3-030-98741-1_2.

M. Tholkapiyan, S. Ramadass, J. Seetha, A. Ravuri, S. S. S, and S. Gore, “Examining the Impacts of Climate Variability on Agricultural Phenology : A Comprehensive Approach Integrating Geoinformatics , Satellite Agrometeorology , and Artificial Intelligence,” ijisae.orgM Tholkapiyan, S Ramadass, J Seetha, A Ravuri, P Vidyullatha, S Siva Shankar, S GoreInternational J. Intell. Syst. Appl. Eng. 2023•ijisae.org, vol. 11, no. 6s, pp. 592–598, 2023, Accessed: Aug. 07, 2023. [Online]. Available: https://www.ijisae.org/index.php/IJISAE/article/view/2891

W. Liu et al., “SSD: Single shot multibox detector,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2016, pp. 21–37. doi: 10.1007/978-3-319-46448-0_2.

X. Ding, C. Hou, and Y. Xu, “Ship Detection in SAR Images Based on an Improved Detector with Rotational Boxes,” in Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 2021, pp. 824–836. doi: 10.1007/978-3-030-89814-4_61.

M. Zhu et al., “Rapid Ship Detection in SAR Images Based on YOLOv3,” in Proceedings - 2020 5th International Conference on Communication, Image and Signal Processing, CCISP 2020, 2020, pp. 214–218. doi: 10.1109/CCISP51026.2020.9273476.

Y. L. Chang, A. Anagaw, L. Chang, Y. C. Wang, C. Y. Hsiao, and W. H. Lee, “Ship detection based on YOLOv2 for SAR imagery,” Remote Sens., vol. 11, no. 7, 2019, doi: 10.3390/rs11070786.

S. Chen, R. Zhan, W. Wang, and J. Zhang, “Learning Slimming SAR Ship Object Detector through Network Pruning and Knowledge Distillation,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 1267–1282, 2021, doi: 10.1109/JSTARS.2020.3041783.

T. Zhang and X. Zhang, “High-speed ship detection in SAR images based on a grid convolutional neural network,” Remote Sens., vol. 11, no. 10, 2019, doi: 10.3390/rs11101206.

Q. Li, L. Mou, Q. Liu, Y. Wang, and X. X. Zhu, “HSF-Net: Multiscale deep feature embedding for ship detection in optical remote sensing imagery,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 12, pp. 7147–7161, 2018, doi: 10.1109/TGRS.2018.2848901.

S. I. T. Joseph, J. Sasikala, and D. Sujitha Juliet, “A novel vessel detection and classification algorithm using a deep learning neural network model with morphological processing (M-DLNN),” Soft Comput., vol. 23, no. 8, pp. 2693–2700, Apr. 2019, doi: 10.1007/s00500-018-3645-4.

K. Rainey, S. Parameswaran, J. Harguess, and J. Stastny, “Vessel classification in overhead satellite imagery using learned dictionaries,” in Applications of Digital Image Processing XXXV, 2012, p. 84992F. doi: 10.1117/12.928875.

R. Grasso, “Ship classification from multi-spectral satellite imaging by convolutional neural networks,” in European Signal Processing Conference, 2019. doi: 10.23919/EUSIPCO.2019.8902822.

Z. Liu, J. Hu, L. Weng, and Y. Yang, “Rotated region based CNN for ship detection,” in Proceedings - International Conference on Image Processing, ICIP, 2018, pp. 900–904. doi: 10.1109/ICIP.2017.8296411.

Z. Guo, L. Zhang, and D. Zhang, “A completed modeling of local binary pattern operator for texture classification,” IEEE Trans. Image Process., vol. 19, no. 6, pp. 1657–1663, 2010, doi: 10.1109/TIP.2010.2044957.

D. A. Clausi and M. Ed Jernigan, “Designing Gabor filters for optimal texture separability,” Pattern Recognit., vol. 33, no. 11, pp. 1835–1849, 2000, doi: 10.1016/S0031-3203(99)00181-8.

Q. Shi, W. Li, F. Zhang, W. Hu, X. Sun, and L. Gao, “Deep CNN with Multi-Scale Rotation Invariance Features for Ship Classification,” IEEE Access, vol. 6, pp. 38656–38668, 2018, doi: 10.1109/ACCESS.2018.2853620.

Y. Feng, W. Diao, X. Sun, M. Yan, and X. Gao, “Towards automated ship detection and category recognition from high-resolution aerial images,” Remote Sens., vol. 11, no. 16, 2019, doi: 10.3390/rs11161901.

G. Margarit and A. Tabasco, “Ship classification in single-pol SAR images based on fuzzy logic,” IEEE Trans. Geosci. Remote Sens., vol. 49, no. 8, pp. 3129–3138, 2011, doi: 10.1109/TGRS.2011.2112371.

R. Touzi, R. K. Raney, and F. Charbonneau, “On the use of permanent symmetric scatterers for ship characterization,” in IEEE Transactions on Geoscience and Remote Sensing, 2004, pp. 2039–2045. doi: 10.1109/TGRS.2004.834654.

G. Margarit, J. J. Mallorquí, and X. Fàbregas, “Single-pass polarimetrie SAR interferometry for vessel classification,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 11, pp. 3494–3502, 2007, doi: 10.1109/TGRS.2007.897437.

A. O. Knapskog, S. Brovoll, and B. Torvik, “Characteristics of ships in harbour investigated in simultaneous images from TerraSAR-X and PicoSAR,” in IEEE National Radar Conference - Proceedings, 2010, pp. 422–427. doi: 10.1109/RADAR.2010.5494583.

C. Zhu, H. Zhou, R. Wang, and J. Guo, “A novel hierarchical method of ship detection from spaceborne optical image based on shape and texture features,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 9, pp. 3446–3456, 2010, doi: 10.1109/TGRS.2010.2046330.

F. Bi, F. Liu, and L. Gao, “A hierarchical salient-region based algorithm for ship detection in remote sensing images,” in Lecture Notes in Electrical Engineering, 2010, pp. 729–738. doi: 10.1007/978-3-642-12990-2_85.

Y. Xia, S. Wan, P. Jin, and L. Yue, “A Novel Sea-Land Segmentation Algorithm Basead on Local Binary Patterns for Ship Detection,” Int. J. Signal Process. Image Process. Pattern Recognit., vol. 7, no. 3, pp. 237–246, 2014, doi: 10.14257/ijsip.2014.7.3.19.

J. Tang, C. Deng, G. Bin Huang, and B. Zhao, “Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine,” IEEE Trans. Geosci. Remote Sens., vol. 53, no. 3, pp. 1174–1185, 2015, doi: 10.1109/TGRS.2014.2335751.

B. LI, X. XIE, X. WEI, and W. TANG, “Ship detection and classification from optical remote sensing images: A survey,” Chinese Journal of Aeronautics, vol. 34, no. 3. pp. 145–163, 2021. doi: 10.1016/j.cja.2020.09.022.

F. Yang, Q. Xu, F. Gao, and L. Hu, “Ship detection from optical satellite images based on visual search mechanism,” in International Geoscience and Remote Sensing Symposium (IGARSS), 2015, pp. 3679–3682. doi: 10.1109/IGARSS.2015.7326621.

Y. Li, S. Zhang, J. Zhao, and W. Tan, “Aircraft Detection in Remote Sensing Images Based on Deep Convolutional Neural Network,” in IOP Conference Series: Earth and Environmental Science, 2019. doi: 10.1088/1755-1315/252/5/052122.

J. Zhao, Z. Zhang, W. Yu, and T. K. Truong, “A Cascade Coupled Convolutional Neural Network Guided Visual Attention Method for Ship Detection from SAR Images,” IEEE Access, vol. 6, pp. 50693–50708, 2018, doi: 10.1109/ACCESS.2018.2869289.

X. T. Vo, T. D. Tran, D. L. Nguyen, and K. H. Jo, “Feature Pyramid Networks for Object Detection,” in Communications in Computer and Information Science, 2021, pp. 168–175. doi: 10.1007/978-3-030-81638-4_13.

P. R. Lei, “Mining maritime traffic conflict trajectories from a massive AIS data,” Knowl. Inf. Syst., vol. 62, no. 1, pp. 259–285, Jan. 2020, doi: 10.1007/s10115-019-01355-0.

D. Filipiak, M. Stró, and W. Krzysztof, “Anomaly Detection in the Maritime Domain: Comparison of Traditional and Big Data Approach,” Sto-Mp-Ist-160, no. June, pp. 1–13, 2018, Accessed: Apr. 26, 2023. [Online]. Available: https://www.sto.nato.int/publications/STO Meeting Proceedings/STO-MP-IST-160/MP-IST-160-S2-5.pdf

K. Sheng, Z. Liu, D. Zhou, A. He, and C. Feng, “Research on Ship Classification Based on Trajectory Features,” J. Navig., vol. 71, no. 1, pp. 100–116, 2018, doi: 10.1017/S0373463317000546.

T. Yang, X. Wang, and Z. Liu, “Ship Type Recognition Based on Ship Navigating Trajectory and Convolutional Neural Network,” J. Mar. Sci. Eng., vol. 10, no. 1, 2022, doi: 10.3390/jmse10010084.

M. Elwakdy, M. El-Bendary, M. Elwakdy, M. El-Bendary, and M. Eltokhy, “A Novel Trajectories Classification Approach for different types of ships using a Polynomial Function and ANFIS,” researchgate.net, 2015, Accessed: Apr. 20, 2023. [Online]. Available: https://www.researchgate.net/profile/Mostafa-Eltokhy/publication/305915945_A_Novel_Trajectories_Classification_Approach_for_different_types_of_ships_using_a_Polynomial_Function_and_ANFIS/links/5c8d2b62299bf14e7e7f6888/A-Novel-Trajectories-Classification-Approach-for-different-types-of-ships-using-a-Polynomial-Function-and-ANFIS.pdf

X. Chen, Y. Liu, K. Achuthan, and X. Zhang, “A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network,” Ocean Eng., vol. 218, 2020, doi: 10.1016/j.oceaneng.2020.108182.

S. Chen, H. Wang, F. Xu, and Y. Q. Jin, “Target Classification Using the Deep Convolutional Networks for SAR Images,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 8, pp. 4806–4817, 2016, doi: 10.1109/TGRS.2016.2551720.

C. Bentes, D. Velotto, and B. Tings, “Ship Classification in TerraSAR-X Images with Convolutional Neural Networks,” IEEE J. Ocean. Eng., vol. 43, no. 1, pp. 258–266, 2018, doi: 10.1109/JOE.2017.2767106.

C. Bentes, D. Velotto, and S. Lehner, “Target classification in oceanographic SAR images with deep neural networks: Architecture and initial results,” in International Geoscience and Remote Sensing Symposium (IGARSS), 2015, pp. 3703–3706. doi: 10.1109/IGARSS.2015.7326627.

R. P. W. Duin and D. M. J. Tax, “Statistical pattern recognition,” in Handbook of Pattern Recognition and Computer Vision, 3rd Edition, 2005, pp. 3–24. doi: 10.1142/9789812775320_0001.

I. Rodriguez-Fdez, A. Canosa, M. Mucientes, and A. Bugarin, “STAC: A web platform for the comparison of algorithms using statistical tests,” in IEEE International Conference on Fuzzy Systems, 2015. doi: 10.1109/FUZZ-IEEE.2015.7337889.

Y. Wang, C. Wang, and H. Zhang, “Ship classification in high-resolution SAR images using deep learning of small datasets,” Sensors (Switzerland), vol. 18, no. 9, 2018, doi: 10.3390/s18092929.

X. Chen, Y. Liu, K. Achuthan, and X. Zhang, “A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network,” Ocean Eng., vol. 218, 2020, doi: 10.1016/j.oceaneng.2020.108182.

X. Yang, H. Sun, X. Sun, M. Yan, Z. Guo, and K. Fu, “Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network,” IEEE Access, vol. 6, pp. 50839–50849, 2018, doi: 10.1109/ACCESS.2018.2869884.

Y. Zhang, L. Guo, Z. Wang, Y. Yu, X. Liu, and F. Xu, “Intelligent ship detection in remote sensing images based on multi-layer convolutional feature fusion,” Remote Sens., vol. 12, no. 20, pp. 1–30, 2020, doi: 10.3390/rs12203316.

H. Deng, X. Sun, M. Liu, C. Ye, and X. Zhou, “Small Infrared Target Detection Based on Weighted Local Difference Measure,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 7, pp. 4204–4214, 2016, doi: 10.1109/TGRS.2016.2538295.

H. He, Y. Lin, F. Chen, H. M. Tai, and Z. Yin, “Inshore Ship Detection in Remote Sensing Images via Weighted Pose Voting,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 6, pp. 3091–3107, 2017, doi: 10.1109/TGRS.2017.2658950.

B. Lebona, W. Kleynhans, T. Celik, and L. Mdakane, “Ship detection using VIIRS sensor specific data,” in International Geoscience and Remote Sensing Symposium (IGARSS), 2016, pp. 1245–1247. doi: 10.1109/IGARSS.2016.7729315.

X. Li, H. Zhao, and L. Zhang, “Recurrent retinaNet: A video object detection model based on focal loss,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2018, pp. 499–508. doi: 10.1007/978-3-030-04212-7_44.

F. Bi, J. Chen, Y. Zhuang, M. Bian, and Q. Zhang, “A decision mixture model-based method for inshore ship detection using high-resolution remote sensing images,” Sensors (Switzerland), vol. 17, no. 7, 2017, doi: 10.3390/s17071470.

R. Xia et al., “CRTransSar: A Visual Transformer Based on Contextual Joint Representation Learning for SAR Ship Detection,” Remote Sens., vol. 14, no. 6, 2022, doi: 10.3390/rs14061488.

C. C. Wackerman, K. S. Friedman, W. G. Pichel, P. Clemente-Colon, and X. Li, “Automatic detection of ships in RADARSAT-1 SAR imagery,” Can. J. Remote Sens., vol. 27, no. 5, pp. 568–577, 2001, doi: 10.1080/07038992.2001.10854896.

F. Y. M. Lure and Y. C. Rau, “Detection of ship tracks in AVHRR cloud imagery with neural networks,” in International Geoscience and Remote Sensing Symposium (IGARSS), 1994, pp. 1401–1403. doi: 10.1109/igarss.1994.399451.

K. Ouchi, “Current Status on Vessel Detection and Classification by Synthetic Aperture Radar for Maritime Security and Safety,” 38th Symp. Remote Sens. Environ. Sci., no. September, pp. 5–12, 2016, Accessed: Apr. 07, 2023. [Online]. Available: https://www.researchgate.net/profile/Kazuo-Ouchi-2/publication/308917393_Current_Status_on_Vessel_Detection_and_Classification_by_Synthetic_Aperture_Radar_for_Maritime_Security_and_Safety/links/5d11dd51299bf1547c7c7fc3/Current-Status-on-Vessel-Detection-a

B. Hirsch and H. Lu, “Automatic target recognition and image analysis,” Artic. CID Number. Teleph. +1, vol. 7495, pp. 676–3290, 2009, Accessed: Apr. 20, 2023. [Online]. Available: https://www.researchgate.net/profile/Zhi-Guo-Cao/publication/252329968_MIPPR_2009_Automatic_Target_Recognition_and_Image_Analysis/links/560151cd08ae07629e52c288/MIPPR-2009-Automatic-Target-Recognition-and-Image-Analysis.pdf

T. Zhang, X. Zhang, J. Shi, and S. Wei, “HyperLi-Net: A hyper-light deep learning network for high-accurate and high-speed ship detection from synthetic aperture radar imagery,” ISPRS J. Photogramm. Remote Sens., vol. 167, pp. 123–153, 2020, doi: 10.1016/j.isprsjprs.2020.05.016.

C. Zhu, H. Zhou, R. Wang, and J. Guo, “A novel hierarchical method of ship detection from spaceborne optical image based on shape and texture features,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 9, pp. 3446–3456, 2010, doi: 10.1109/TGRS.2010.2046330.

A. Mehran, S. Tehsin, and M. Hamza, “An effective deep learning model for ship detection from satellite images,” Spat. Inf. Res., vol. 31, no. 1, pp. 61–72, Feb. 2023, doi: 10.1007/S41324-022-00482-1.

C. Yuan and D. Casasent, “A new SVM for distorted SAR object classification,” in Optical Pattern Recognition XVI, 2005, p. 10. doi: 10.1117/12.607979.

Q. Zhao and J. C. Principe, “Support vector machines for SAR automatic target recognition,” IEEE Trans. Aerosp. Electron. Syst., vol. 37, no. 2, pp. 643–654, 2001, doi: 10.1109/7.937475.

Y. Lin and B. Bhanu, “Evolutionary feature synthesis for object recognition,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 35, no. 2, pp. 156–171, 2005, doi: 10.1109/TSMCC.2004.841912.

B. Bhanu and Y. Lin, “Genetic algorithm based feature selection for target detection in SAR images,” in Image and Vision Computing, 2003, pp. 591–608. doi: 10.1016/S0262-8856(03)00057-X.

O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, Dec. 2015, doi: 10.1007/s11263-015-0816-y.

J. Li, C. Xu, H. Su, L. Gao, and T. Wang, “Deep Learning for SAR Ship Detection: Past, Present and Future,” Remote Sens., vol. 14, no. 11, 2022, doi: 10.3390/rs14112712.

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science (80-. )., vol. 313, no. 5786, pp. 504–507, Jul. 2006, doi: 10.1126/science.1127647.

I. G. and Y. B. and A. Courville, “Deep learning,” Nature, vol. 29, no. 7553, pp. 1–73, 2016, Accessed: Jan. 16, 2023. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=omivDQAAQBAJ&oi=fnd&pg=PR5&dq=Goodfellow+et+al.,+2016&ots=MNT1irsBWW&sig=ExqJ1S6mUSSkGQwQj3J4_jzJms8

K. Nogueira, O. A. B. Penatti, and J. A. dos Santos, “Towards better exploiting convolutional neural networks for remote sensing scene classification,” Pattern Recognit., vol. 61, pp. 539–556, 2017, doi: 10.1016/j.patcog.2016.07.001.

H. Greidanus, M. Alvarez, C. Santamaria, F. X. Thoorens, N. Kourti, and P. Argentieri, “The SUMO ship detector algorithm for satellite radar images,” Remote Sens., vol. 9, no. 3, 2017, doi: 10.3390/rs9030246.

Z. Zou and Z. Shi, “Ship Detection in Spaceborne Optical Image with SVD Networks,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 10, pp. 5832–5845, 2016, doi: 10.1109/TGRS.2016.2572736.

H. Lin, Z. Shi, and Z. Zou, “Fully Convolutional Network with Task Partitioning for Inshore Ship Detection in Optical Remote Sensing Images,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 10, pp. 1665–1669, 2017, doi: 10.1109/LGRS.2017.2727515.

W. Han et al., “Methods for Small, Weak Object Detection in Optical High-Resolution Remote Sensing Images: A survey of advances and challenges,” IEEE Geosci. Remote Sens. Mag., vol. 9, no. 4, pp. 8–34, 2021, doi: 10.1109/MGRS.2020.3041450.

Q. Wang, H. Huang, A. Yu, and Z. Dong, “An efficient and adaptive approach for noise filtering of SAR interferometric phase images,” IEEE Geosci. Remote Sens. Lett., vol. 8, no. 6, pp. 1140–1144, 2011, doi: 10.1109/LGRS.2011.2158289.

X. Xing, K. Ji, H. Zou, W. Chen, and J. Sun, “Ship classification in terra SAR-X images with feature space based sparse representation,” IEEE Geosci. Remote Sens. Lett., vol. 10, no. 6, pp. 1562–1566, 2013, doi: 10.1109/LGRS.2013.2262073.

V. F. Arguedas, “TEXTURE-BASED VESSEL CLASSIFIER FOR ELECTRO-OPTICAL SATELLITE IMAGERY Virginia Fernandez Arguedas European Commission - Joint Research Centre ( JRC ) Via E . Fermi 2749 , 21020 - Ispra , Italy,” Int. Conf. Image Process., pp. 1–5, 2015.

H. Yao, L. Chuyi, H. Dan, and Y. Weiyu, “Gabor Feature Based Convolutional Neural Network for Object Recognition in Natural Scene,” Proc. - 2016 3rd Int. Conf. Inf. Sci. Control Eng. ICISCE 2016, pp. 386–390, 2016, doi: 10.1109/ICISCE.2016.91.

T. Zhang and X. Zhang, “Squeeze-and-Excitation Laplacian Pyramid Network with Dual-Polarization Feature Fusion for Ship Classification in SAR Images,” IEEE Geosci. Remote Sens. Lett., vol. 19, 2022, doi: 10.1109/LGRS.2021.3119875.

H. Heiselberg and A. Stateczny, “Remote sensing in vessel detection and navigation,” Sensors (Switzerland), vol. 20, no. 20. pp. 1–5, 2020. doi: 10.3390/s20205841.

W. Yang, X. Yin, and G. S. Xia, “Learning high-level features for satellite image classification with limited labeled samples,” IEEE Trans. Geosci. Remote Sens., vol. 53, no. 8, pp. 4472–4482, 2015, doi: 10.1109/TGRS.2015.2400449.

X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Journal of Machine Learning Research, 2011, pp. 315–323. Accessed: Apr. 20, 2023. [Online]. Available: http://proceedings.mlr.press/v15/glorot11a

G. Cheng, J. Han, P. Zhou, and L. Guo, “Multi-class geospatial object detection and geographic image classification based on collection of part detectors,” ISPRS J. Photogramm. Remote Sens., vol. 98, pp. 119–132, 2014, doi: 10.1016/j.isprsjprs.2014.10.002.

“Airbus Ship Detection Challenge Kaggle.” https://www.google.com/search?q=Kaggle.+Airbus+ship+detection+challenge%5BInternet%5D%3B+Available+from%3A+https%3A%2F%2Fwww.kaggle.com%2Fc%2Fairbus-ship-detection%2Fdata.&rlz=1C1CHBF_enIN1045IN1045&oq=Kaggle.+Airbus+ship+detection+challenge%5BInternet%5D (accessed Apr. 07, 2023).

K. Chen, M. Wu, J. Liu, and C. Zhang, “FGSD: A Dataset for Fine-Grained Ship Detection in High Resolution Satellite Images,” Mar. 2020, Accessed: Apr. 07, 2023. [Online]. Available: http://arxiv.org/abs/2003.06832

Umbarkar, A. M., Sherie, N. P., Agrawal, S. A., Kharche, P. P., & Dhabliya, D. (2021). Robust design of optimal location analysis for piezoelectric sensor in a cantilever beam. Materials Today: Proceedings, doi:10.1016/j.matpr.2020.12.1058

Downloads

Published

16.08.2023

How to Cite

Pawar , S. J. ., & Gandhe, S. T. . (2023). SAR (Synthetic Aperture Radar) Image Study and Analysis for Object Recognition in Surveillance. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 552–573. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3311

Issue

Vol. 11 No. 10s (2023)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.