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A Benchmarking Protocol for SAR Colorization: From Regression to Deep Learning Approaches (2310.08705v1)

Published 12 Oct 2023 in cs.CV and eess.IV

Abstract: Synthetic aperture radar (SAR) images are widely used in remote sensing. Interpreting SAR images can be challenging due to their intrinsic speckle noise and grayscale nature. To address this issue, SAR colorization has emerged as a research direction to colorize gray scale SAR images while preserving the original spatial information and radiometric information. However, this research field is still in its early stages, and many limitations can be highlighted. In this paper, we propose a full research line for supervised learning-based approaches to SAR colorization. Our approach includes a protocol for generating synthetic color SAR images, several baselines, and an effective method based on the conditional generative adversarial network (cGAN) for SAR colorization. We also propose numerical assessment metrics for the problem at hand. To our knowledge, this is the first attempt to propose a research line for SAR colorization that includes a protocol, a benchmark, and a complete performance evaluation. Our extensive tests demonstrate the effectiveness of our proposed cGAN-based network for SAR colorization. The code will be made publicly available.

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References (50)
  1. A global quality measurement of pan-sharpened multispectral imagery. IEEE Geoscience and Remote Sensing Letters 1, 313–317.
  2. Influential observations, high leverage points, and outliers in linear regression. Statistical science , 379–393.
  3. Reusing discriminators for encoding: Towards unsupervised image-to-image translation, in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE. pp. 8165–8174.
  4. Additive integration of sar features into multispectral spot images by means of the à trous wavelet decomposition. ISPRS journal of photogrammetry and remote sensing 60, 306–314.
  5. A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing 171, 101–117.
  6. Colorization for polarimetric sar image based on scattering mechanisms, in: 2008 Congress on Image and Signal Processing, IEEE. pp. 697–701.
  7. Learning diverse image colorization, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2877–2885.
  8. Applied regression analysis. volume 326. John Wiley & Sons.
  9. Sen12ms-cr: Multi-sensor data fusion for cloud removal in global and all-season sentinel-2 imagery. IEEE Transactions on Geoscience and Remote Sensing 59, 5866–5878.
  10. Generative adversarial networks. Communications of the ACM 63, 139–144.
  11. Convolutional neural networks at constrained time cost, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5353–5360.
  12. Image-to-image translation with conditional adversarial networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1125–1134.
  13. Sar image colorization using multidomain cycle-consistency generative adversarial network. IEEE Geoscience and Remote Sensing Letters 18, 296–300.
  14. A fusion method of optical image and sar image based on dense-ugan and gram-schmidt transformation. Remote Sensing 13, 4274.
  15. The method for colorizing sar images of kompsat-5 using cycle gan with multi-scale discriminators. Korean Journal of Remote Sensing 34, 1415–1425.
  16. Pixel level fusion techniques for sar and optical images: A review. Information Fusion 59, 13–29.
  17. Design of cyclegan model for sar image colorization, in: 2021 IEEE VTS 17th Asia Pacific Wireless Communications Symposium (APWCS), pp. 1–5.
  18. Labeling dataset based colorization of sar images using cycle gan. The Journal of Korean Institute of Electromagnetic Engineering and Science 33, 776–783.
  19. Deep learning in multimodal remote sensing data fusion: A comprehensive review. International Journal of Applied Earth Observation and Geoinformation 112, 102926.
  20. Unsupervised image-to-image translation networks. Advances in neural information processing systems 30.
  21. Remote sensing data fusion with generative adversarial networks: State-of-the-art methods and future research directions. IEEE Geoscience and Remote Sensing Magazine 10, 295–328.
  22. Psgan: A generative adversarial network for remote sensing image pan-sharpening. IEEE Transactions on Geoscience and Remote Sensing 59, 10227–10242.
  23. Haze correction for contrast-based multispectral pansharpening. IEEE Geoscience and Remote Sensing Letters 14, 2255–2259.
  24. Experimental validation of surface scattering and emission models. IEEE Transactions on Geoscience and Remote Sensing 38, 459–469.
  25. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 .
  26. Pancolorgan:rethinking cnn-based pansharpening: Guided colorization of panchromatic images via gans. IEEE Transactions on Geoscience and Remote Sensing 59, 3486–3501.
  27. U-net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, Springer. pp. 234–241.
  28. Colorizing sentinel-1 sar images using a variational autoencoder conditioned on sentinel-2 imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 1045–1051.
  29. Sen12: The sen1-2 dataset for deep learning in sar-optical data fusion. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1, 141–146.
  30. Fusion of sar and optical remote sensing data: Challenges and recent trends, in: 2017 IEEE International Geoscience and Remote Sensing Symposium, pp. 5458–5461.
  31. A fast intensity-hue-saturation fusion technique with spectral adjustment for ikonos imagery. IEEE Geoscience and Remote sensing letters 1, 309–312.
  32. Multispectral and hyperspectral image fusion in remote sensing: A survey. Information Fusion 89, 405–417.
  33. A critical comparison among pansharpening algorithms. IEEE Transactions on Geoscience and Remote Sensing 53, 2565–2586.
  34. A benchmarking protocol for pansharpening: Dataset, preprocessing, and quality assessment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, 6102–6118.
  35. A new benchmark based on recent advances in multispectral pansharpening: Revisiting pansharpening with classical and emerging pansharpening methods. IEEE Geoscience and Remote Sensing Magazine 9, 53–81.
  36. Real-esrgan: Training real-world blind super-resolution with pure synthetic data, in: International Conference on Computer Vision Workshops (ICCVW).
  37. Esrgan: Enhanced super-resolution generative adversarial networks, in: The European Conference on Computer Vision Workshops (ECCVW).
  38. A universal image quality index. IEEE signal processing letters 9, 81–84.
  39. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13, 600–612.
  40. Hybrid cgan: Coupling global and local features for sar-to-optical image translation. IEEE Transactions on Geoscience and Remote Sensing 60, 1–16.
  41. Fg-gan: A fine-grained generative adversarial network for unsupervised sar-to-optical image translation. IEEE Transactions on Geoscience and Remote Sensing 60, 1–11.
  42. An unsupervised sar and optical image fusion network based on structure-texture decomposition. IEEE Geoscience and Remote Sensing Letters 19, 1–5.
  43. Discrimination among semi-arid landscape endmembers using the spectral angle mapper (sam) algorithm, in: JPL, Summaries of the Third Annual JPL Airborne Geoscience Workshop. Volume 1: AVIRIS Workshop.
  44. Multispectral and sar image fusion based on laplacian pyramid and sparse representation. Remote Sensing 14, 870.
  45. Image fusion meets deep learning: A survey and perspective. Information Fusion 76, 323–336.
  46. Colorful image colorization, in: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14, Springer. pp. 649–666.
  47. Pgman: An unsupervised generative multiadversarial network for pansharpening. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, 6316–6327.
  48. Ucgan: Unsupervised cycle-consistent generative adversarial networks for pan sharpening. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14.
  49. Deep learning meets sar: Concepts, models, pitfalls, and perspectives. IEEE Geoscience and Remote Sensing Magazine 9, 143–172.
  50. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine 5, 8–36.
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