volume | PIER Journals

Abstract

This research proposes a novel method based on generative adversarial network (GAN) and hidden Markov random field (HMRF) models, for use in large-scale high-resolution synthetic aperture radar (SAR) images. The method consists of three stages. In the first stage, a virtual target and a SAR image are generated by using the GAN model, according to the statistical and gray-level features of the original SAR image used in detection. In the second stage, the virtual target is embedded in the generated image. In the third stage, real targets are detected in the generated image by using the HMRF model. The experiment results show that the proposed algorithm based on GAN and HMRF models can be applied to ship detection in high-resolution SAR images, with high accuracy and processing speed.

1. Pappas, O., A. Achim, and D. Bull, "Superpixel-level CFAR detectors for ship detection in SAR imagery," IEEE Geosci. Remote Sens. Lett., Vol. 15, No. 9, 1397-1401, 2018.
doi:10.1109/LGRS.2018.2838263 Google Scholar

2. Gao, G., S. Gao, J. He, and G. Li, "Adaptive ship detection in hybrid-polarimetric SAR images based on the powerCentropy decomposition," IEEE Trans. Geosci. Remote Sens., Vol. 56, No. 9, 5394-5407, 2018.
doi:10.1109/TGRS.2018.2815592 Google Scholar

3. Leng, X., K. Ji, X. Xing, S. Zhou, and H. Zou, "Area ratio invariant feature group for ship detection in SAR imagery," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., Vol. 11, No. 7, 2376-2388, 2018.
doi:10.1109/JSTARS.2018.2820078 Google Scholar

4. Ai, J., X. Yang, J. Song, Z. Dong, L. Jia, and F. Zhou, "An adaptively truncated clutter-statistics-based two-parameter CFAR detector in SAR imagery," IEEE J. Oceanic Eng., Vol. 43, No. 1, 267-279, 2018.
doi:10.1109/JOE.2017.2768198 Google Scholar

5. Li, T., Z. Liu, R. Xie, and L. Ran, "An improved superpixel-level CFAR detection method for ship targets in high-resolution SAR images," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., Vol. 11, No. 1, 184-194, 2018.
doi:10.1109/JSTARS.2017.2764506 Google Scholar

6. Gao, G., S. Gao, J. He, and G. Li, "Ship detection using compact polarimetric SAR based on the notch filter," IEEE Trans. Geosci. Remote Sens., Vol. 56, No. 9, 5380-5393, 2018.
doi:10.1109/TGRS.2018.2815582 Google Scholar

7. Zhan, Y., D. Hu, Y. Wang, and X. Yu, "Semisupervised hyperspectral image classification based on generative adversarial networks," IEEE Geosci. Remote Sens. Lett., Vol. 15, No. 2, 212-216, 2018.
doi:10.1109/LGRS.2017.2780890 Google Scholar

8. Ghamisi, P., J. Benediktsson, and M. Ulfarsson, "Spectral-spatial classification of hyperspectral images based on hidden markov random fields," IEEE Trans. Geosci. Remote Sens., Vol. 52, No. 5, 2565-2574, 2016.
doi:10.1109/TGRS.2013.2263282 Google Scholar

9. Gao, G., Characterization of SAR Clutter and Its Applications to Land and Ocean Observations, Springer, 2019.
doi:10.1007/978-981-13-1020-1

Prof. Meng Yang

Dr. Chenchen Yi