-
CiteScore
-
Impact Factor
IECE Journal of Image Analysis and Processing, 2024, Volume 1, Issue 1: 6-20

Free Access | Research Article | 08 December 2024
1 Department of Computer Science, Virtual University, Pakistan
2 Department of Computer and Software Technology, University of Swat, KP, Pakistan
3 School of Computer Science, Gran Sasso Science Institute (GSSI), L’Aquila, Italy
* Corresponding author: Muzammil Khan, email: [email protected]
Received: 02 October 2024, Accepted: 04 November 2024, Published: 08 December 2024  

Abstract
This paper presents an ensemble approach for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) that integrates AlexNet, Support Vector Machine (SVM), and template matching through majority voting to improve classification accuracy under various operating conditions. The study utilizes the MSTAR dataset, focusing on both Standard Operating Conditions (SOC) and Extended Operating Conditions (EOC). The methodology begins with SAR image preprocessing, applying threshold segmentation with histogram equalization and morphological filtering to extract target regions. These regions undergo feature extraction, with AlexNet and SVM separately classifying the targets, while template matching identifies test images by comparing binary target regions with template images. Experimental results demonstrate AlexNet's superior classification under SOC and EOC-1, though template matching showed better performance for specific targets like 2S1 in EOC-1. The AlexNet based approach achieved average accuracies of 90% in SOC and 87% in EOC-1, showing significant improvement over individual techniques. The study concludes that the ensemble technique effectively enhances SAR ATR accuracy and highlights the benefits of improved data preprocessing and feature extraction for classification stages. This research contributes to ongoing advancements in SAR ATR by optimizing classifier performance in challenging conditions and establishing ensemble techniques as a promising direction for robust target recognition.

Graphical Abstract
AlexNet based Ensembel Approach for Synthetic Aperture Radar Target Classification under Different Conditions

Keywords
ensemble classifier
synthetic aperture radar (SAR)
automatic target recognition (ATR)
standard operating condition (SOC)
extended operating condition (EOC)
image classification

References

[1] El-Darymli, K., Gill, E. W., Mcguire, P., Power, D., & Moloney, C. (2016). Automatic target recognition in synthetic aperture radar imagery: A state-of-the-art review. IEEE access, 4, 6014-6058.

[2] Shi, C., Miao, F., Jin, Z., & Xia, Y. (2019). Target recognition of synthetic aperture radar images based on matching and similarity evaluation between binary regions. IEEE Access, 7, 154398–154413.

[3] Jiang, C., & Zhou, Y. (2018). Hierarchical fusion of convolutional neural networks and attributed scattering centers with application to robust SAR ATR. Remote Sensing, 10(6), 819.

[4] Rahman, N., Khan, M., Ullah, I., & Kim, D. H. (2024). A novel lightweight CNN for constrained IoT devices: Achieving high accuracy with parameter efficiency on the MSTAR dataset. IEEE Access, 12, 6014–6058.

[5] Shi, X., Zhou, F., Yang, S., Zhang, Z., & Su, T. (2019). Automatic target recognition for synthetic aperture radar images based on super-resolution generative adversarial network and deep convolutional neural network. Remote Sensing, 11(2), 135.

[6] Al Mufti, M., Al Hadhrami, E., Taha, B., & Werghi, N. (2018). Automatic target recognition in SAR images: Comparison between pre-trained CNNs in a transfer learning based approach. In 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD) (pp. 160–164). IEEE.

[7] Bolourchi, P., Moradi, M., Demirel, H., & Uysal, S. (2020). Ensembles of classifiers for improved SAR image recognition using pseudo Zernike moments. The Journal of Defense Modeling and Simulation, 17(2), 205–211.

[8] Savitha, R., & Ramakanthkumar, P. (2012). Automatic target recognition of SAR images using radial features and SVM. International Journal of Computer Science and Network Security (IJCSNS), 12(2), 52.

[9] Zhai, Y., Deng, W., Zhu, Y., Xu, Y., Sun, B., Li, J., Ke, Q., Zhi, Y., & Pirui, V. (2019). A novel lightweight SARNet with clock-wise data amplification for SAR ATR. Progress in Electromagnetics Research, 91, 69–82.

[10] Tian, S., Wang, C., & Zhang, H. (2019). SAR object classification with a multi-scale convolutional auto-encoder. In 2019 SAR in Big Data Era (BIGSARDATA) (pp. 1–4). IEEE.

[11] Lv, J., & Liu, Y. (2019). Data augmentation based on attributed scattering centers to train robust CNN for SAR ATR. IEEE Access, 7, 25459–25473.

[12] Kwak, Y., Song, W. J., & Kim, S. E. (2018). Speckle-noise-invariant convolutional neural network for SAR target recognition. IEEE Geoscience and Remote Sensing Letters, 16(4), 549-553.

[13] Khan, S. S., Ran, Q., & Khan, M. (2020). Image pan-sharpening using enhancement-based approaches in remote sensing. Multimedia Tools and Applications, 79(43), 32791-32805.

[14] Chen, S., Wang, H., Xu, F., & Jin, Y. Q. (2016). Target classification using the deep convolutional networks for SAR images. IEEE Transactions on Geoscience and Remote Sensing, 54(8), 4806–4817.

[15] Fan, J., & Tomas, A. (2018). Target reconstruction based on attributed scattering centers with application to robust SAR ATR. Remote Sensing, 10(4), 655.

[16] Tan, J., Fan, X., Wang, S., & Ren, Y. (2018). Target recognition of SAR images via matching attributed scattering centers with binary target region. Sensors, 18(9), 3019.

[17] Ding, B., Wen, G., Huang, X., Ma, C., & Yang, X. (2017). Target recognition in synthetic aperture radar images via matching of attributed scattering centers. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(7), 3334–3347.

[18] Ding, B., Wen, G., Zhong, J., Ma, C., & Yang, X. (2017). A robust similarity measure for attributed scattering center sets with application to SAR ATR. Neurocomputing, 219, 130–143.

[19] Tang, T., & Su, Y. (2012). Object recognition based on feature matching of scattering centers in SAR imagery. In 2012 5th International Congress on Image and Signal Processing (pp. 1073–1076). IEEE.

[20] Ding, B., & Wen, G. (2018). A region matching approach based on 3-d scattering center model with application to SAR target recognition. IEEE Sensors Journal, 18(11), 4623–4632.

[21] Feng, B., Tang, W., & Feng, D. (2020). Target recognition of SAR images via hierarchical fusion of complementary features. Optik, 217, 164695.

[22] Sikai, L., & Jun, Y. (2018). A satellite-borne SAR target recognition method based on supplementary feature fusion. In 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 326–330). IEEE.

[23] Khan, S. S., Khan, M., & Alharbi, Y. (2020). Multi-focus image fusion using image enhancement techniques with wavelet transformation. International Journal of Advanced Computer Science and Applications (IJACSA), 11(5).

[24] Cui, Z., Cao, Z., Yang, J., & Feng, J. (2013). A hierarchical propelled fusion strategy for SAR automatic target recognition. EURASIP Journal on Wireless Communications and Networking, 2013(1), 1–8.

[25] Zhang, J., Xing, M., & Xie, Y. (2020). FEC: A feature fusion framework for SAR target recognition based on electromagnetic scattering features and deep CNN features. IEEE Transactions on Geoscience and Remote Sensing, 59(3), 2174-2187.

[26] Zhang, F., Wang, Y., Ni, J., Zhou, Y., & Hu, W. (2019). SAR target small sample recognition based on CNN cascaded features and adaboost rotation forest. IEEE Geoscience and Remote Sensing Letters, 17(6), 1008–1012.

[27] Xue, Y., Pei, J., Huang, Y., Yang, J., & Zhang, Y. (2018). Target recognition for SAR images based on heterogeneous CNN ensemble. In 2018 IEEE Radar Conference (RadarConf18) (pp. 0507–0512). IEEE.

[28] Balan, P. S., & Sunny, L. E. (2018). Survey on feature extraction techniques in image processing. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 6(III).

[29] Khan, S. S., Ran, Q., & Khan, M. (2020). Image pan-sharpening using enhancement-based approaches in remote sensing. Multimedia Tools and Applications, 79(43), 32791-32805.

[30] Park, J. I., Park, S. H., & Kim, K. T. (2012). New discrimination features for SAR automatic target recognition. IEEE Geoscience and Remote Sensing Letters, 10(3), 476-480.

[31] He, Z., Xiao, H., & Liu, R. (2019). Synthetic aperture radar target recognition based on multidimensional sparse model. In Journal of Physics: Conference Series, 1169, 012026. IOP Publishing.

[32] Zhang, X., Liu, Z., Liu, S., Li, D., Jia, Y., & Huang, P. (2017). Sparse coding of 2d-slice Zernike moments for SAR ATR. International Journal of Remote Sensing, 38(2), 412–431.

[33] Gorovyi, I. M., & Sharapov, D. S. (2017). Efficient object classification and recognition in SAR imagery. In 2017 18th International Radar Symposium (IRS) (pp. 1-5). IEEE.

[34] Coman, C. (2018). A deep learning SAR target classification experiment on MSTAR dataset. In 2018 19th International Radar Symposium (IRS) (pp. 1-6). IEEE.

[35] Belloni, C., Aouf, N., Le Caillec, J.-M., & Merlet, T. (2019). Comparison of descriptors for SAR ATR. In 2019 IEEE Radar Conference (RadarConf) (pp. 1-6). IEEE.

[36] Bolourchi, P., Moradi, M., Demirel, H., & Uysal, S. (2020). Improved SAR target recognition by selecting moment methods based on Fisher score. Signal, Image and Video Processing, 14, 39-47.

[37] Zhang, X., Liu, Z., Liu, S., Li, D., Jia, Y., & Huang, P. (2017). Sparse coding of 2D-slice Zernike moments for SAR ATR. International Journal of Remote Sensing, 38(2), 412-431.

[38] Zaied, S., Toumi, A., & Khenchaf, A. (2018). Target classification using convolutional deep learning and auto-encoder models. In 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (pp. 1-6). IEEE.


Cite This Article
APA Style
Rahman, N., Khan, M., & Khan, I. (2024). AlexNet based Ensembel Approach for Synthetic Aperture Radar Target Classification under Different Conditions. IECE Journal of Image Analysis and Processing, 1(1), 6–20. https://doi.org/10.62762/JIAP.2024.927304

Article Metrics
Citations:

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 74
PDF Downloads: 15

Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions
IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
IECE Journal of Image Analysis and Processing

IECE Journal of Image Analysis and Processing

ISSN: request pending (Online)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.