Abstract
Due to its good performance, synthetic aperture radar (SAR) is gradually regarded as a favorable tool to solve the target detection task, especially in marine target detection. Maritime target detection models always need a lot of SAR images for learning, but in fact the use of SAR images has limitations so the acquisition of SAR images is a problem. In addition to this, the labeling of the target is also a time consuming and labor intensive process. In order to solve these problems, we propose a location-based SAR generation model, referred to as the LocSAR, which takes the target position and category information as inputs and outputs location-specific high-quality complex SAR images. The model consists of four parts: a cascade Encoder-Decoder, a scene fusion module, a feature fusion module and a discriminator. We use the feature fusion module to fuse the features of neighboring resolution images and introduce the scene fusion module for semantic information learning. The Encoder-Decoder implements feature extraction and reconstruction, while the discriminator is used for adversarial training. We also use gradient variance loss to improve the quality of the generated images. We updated the existing data set HRSID with the addition of land and sea labels and conducted experiments on this. Extensive experiments show that our model is excellent to classic image generation models. We have also designed a simple interface to assist in image generation and labeling, which can be directly applied to the other model.
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No datasets were generated or analysed during the current study.
References
Zhang, Y., Hua, Q., Jiang, Y., Li, H., Xu, D.: CV-MotionNet: Complex-Valued Convolutional Neural Network for SAR Moving Ship Targets Classification. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, pp. 4280–4283 (2021)
Ju, M., Niu, B., Hu., Q.: SARGAN: A novel SAR Image Generation Method for SAR Ship Detection Task. IEEE Sens. J. 23(22), 28500–28512 (2023). 15 Nov.15
Jiang, L., Wang, Z., -x, W.: Yu.: Model based building height retrieval from single SAR images. 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, Chongqing, China, pp. 379–384 (2011)
Zhang, M., Fan, W., Li, J.: Numerical Simulation and Analyses of SAR Images from Moving Ships over a Sea Surface. 2018 IEEE International Symposium on Antennas and, Propagation: & USNC/URSI National Radio Science Meeting, Boston, MA, USA, pp. 573–574 (2018)
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., Bengio, Y.: Generative adversarial networks. Commun. ACM. 63, 139–144 (2014)
Du, S., Hong, J., Wang, Y., Qi., Y.: A High-Quality Multicategory SAR Images Generation Method With Multiconstraint GAN for ATR. in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, Art no. 4011005 (2022)
Wei, S., Zeng, X., Qu, Q., Wang, M., Su, H., Shi., J.: HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation. in IEEE Access, vol. 8, pp. 120234–120254 (2020)
Li, B., Liu, B., Huang, L., Guo, W., Zhang, Z.: 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), pp. 1–5. Beijing, China (2017). and W. Yu.: OpenSARShip 2.0: A large-volume dataset for deeper interpretation of ship targets in Sentinel-1 imagery
Chen, Z., Zeng, Z., Huang, Y., Wan, J., Tan, X.: SAR raw data simulation for fluctuant terrain: A new shadow judgment method and simulation result evaluation framework. IEEE Trans. Geosci. Remote Sens. 60, Art5215018 (2022)
Huo, W., Huang, Y., Pei, J., Zhang, Y., Yang., J.: A New SAR Image Simulation Method for Sea-Ship Scene. IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, pp. 721–724 (2018)
Li, K., Luan, S.: and D. Zhou.:An Optical-to-SAR Transformation Method for SAR Ship Image Augmentation. IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP), Shanghai, China, pp. 264–268 (2020) (2020)
Kingma, D.P., Welling, M.: Auto-Encoding Variational Bayes. CoRR. arXiv preprint arXiv:1312.6114 (2013)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840–6851 (2020)
Zeng, Z.-B., Sun, S.-K., He, Z.: and D. -Z. Ding.: A Few-shot learning SAR image generative model for automatic target recognition. 2023 International Applied Computational Electromagnetics Society Symposium (ACES-China), Hangzhou, China, pp. 1–3 (2023)
Arjovsky, M.: Soumith Chintala, and Léon Bottou.: Wasserstein generative adversarial networks. International conference on machine learning. PMLR (2017)
Radford, A.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv Preprint arXiv:151106434 (2015)
Li, L., Wang, C., Zhang, H., Zhang., B.: SAR Image Ship Object Generation and Classification With Improved Residual Conditional Generative Adversarial Network. in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, Art no. 4000105 (2022)
Peng, G., Liu, M., Chen, S., Li, Y., Lu., F.: Generation of SAR Images with Features for Target Recognition. 2022 IEEE International Conference on Signal Processing, Communications and, Computing: (ICSPCC), Xi’an, China, pp. 1–4 (2022)
Liu, J., Zhang, T., Xiong., H.: PolSAR Ship targets Generation via the Polarimetric feature guided Denoising Diffusion Probabilistic Model. in IEEE Geosci. Remote Sens. Lett. (2024)
Niu, A., et al.: CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution. IEEE International Conference on Image Processing (ICIP), Kuala Lumpur, Malaysia, pp. 615–619 (2023) (2023)
Newell, A., et al.: Stacked Hourglass Networks for Human Pose Estimation. European Conference on Computer Vision (2016)
Tang, L., et al.: Rethinking the necessity of image fusion in high-level vision tasks: A practical infrared and visible image fusion network based on progressive semantic injection and scene fidelity. Inf. Fusion. 99, 101870 (2023)
Abrahamyan, L., Truong, A.M., Philips, W., Deligiannis, N.: Gradient Variance Loss for Structure-Enhanced Image Super-Resolution. ICASSP 2022–2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3219–3223 (2022)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv Preprint arXiv:14111784 (2014)
Horé, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. in Proc. 20th Int. Conf. Pattern Recognit., Aug. pp. 2366–2369 (2010)
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 586–595 (2018)
Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. pp. 5967–5976 (2017)
Xie, X., Cheng, G., Wang, J., Yao, X., Han, J.: Oriented R-CNN for Object Detection. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 3500–3509 (2021)
Li, W., Zhu, J.: Oriented RepPoints for Aerial Object Detection. 2022 IEEE/CVF Conference on Computer Vision and, Recognition, P.: (CVPR), 1819–1828 (2021)
Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 62201114 and Grant 62301108; in part by the Scientific Research Fund of Liaoning Provincial Education Department under Grant JYTMS20230171 and Grant JYTMS20230176; in part by the Fundamental Research Funds for the Central Universities under Grant 3132024237.
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M. J. and M. L. contributed to this work by designing the algorithms, analyzing the data and writing the paper. T. M. and N. N. contributed to set up the experimental environment and performed the experiments. D. F. and S. J. contributed through research supervisory and reviewer role by amending the paper.
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Ju, M., Li, M., Mao, T. et al. LocSAR: a location-based SAR ship generation model for complex backgrounds. SIViP 19, 78 (2025). https://doi.org/10.1007/s11760-024-03605-3
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DOI: https://doi.org/10.1007/s11760-024-03605-3