期刊
IEEE ACCESS
卷 8, 期 -, 页码 217554-217572出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3042213
关键词
Optical imaging; Optical sensors; Nonlinear optics; Image segmentation; Image matching; Optical distortion; Adaptive optics; Image matching; image synthesis; synthetic aperture radar (SAR); generative adversarial networks (GANs)
资金
- National Natural Science Foundation of China [61572505, 62002360, 61806206, 61772530, U1610124]
- Six Talent Peaks Project in Jiangsu Province [2015-DZXX-010]
- Natural Science Foundation of Jiangsu Province [BK20180639, BK20171192]
- China Postdoctoral Science Foundation [2018M642359]
- Science and Technology Development Fund of Macau [0038/2020/A1]
- Science and Technology Development Fund, Macu SAR under the open project of State Key Laboratory of Lunar and Planetary Science
- Fundamental Research Funds for the Central Universities [2020ZDPY0305]
- Xuzhou Science and Technology Project [KC18061]
Synthetic Aperture Radar and optical (SAR-optical) image matching is a technique of finding correspondences between SAR and optical images. SAR-optical image matching can be simplified to single-mode image matching through image synthesis. However, the existing SAR-optical image synthesis methods are unable to provide qualified images for SAR-optical image matching. In this work, we present a K-means Clustering Guide Generative Adversarial Networks (KCG-GAN) to improve the image quality of synthesizing by constraining spatial information synthesis. KCG-GAN uses k-means segmentations as one of the image generator's inputs and introduces feature matching loss, segmentation loss, and L1 loss to the objective function. Meanwhile, to provide repeatable k-means segmentations, we develop a straightforward 1D k-means algorithm. We compare KCG-GAN with a leading image synthesis method-pix2pixHD. Qualitative results illustrate that KCG-GAN preserves more spatial structures than pix2pixHD. Quantitative results show that, compared with pix2pixHD, images synthesized by KCG-GAN are more similar to original optical images, and SAR-optical image matching based on KCG-GAN obtains at most 3.15 times more qualified matchings. Robustness tests demonstrate that SAR-optical image matching based on KCG-GAN is robust to rotation and scale changing. We also test three SIFT-like algorithms on matching original SAR-optical image pairs and matching KCG-GAN synthesized optical-optical image pairs. Experimental results show that our KCG-GAN significantly improves the performances of the three algorithms on SAR-optical image matching.
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