{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T09:31:36Z","timestamp":1778319096362,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T00:00:00Z","timestamp":1727568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangxi Province Key Laboratory of Electronic Data Control and Forensics (Jinggangshan University)","award":["20242BCC32027"],"award-info":[{"award-number":["20242BCC32027"]}]},{"name":"Jiangxi Province Key Laboratory of Electronic Data Control and Forensics (Jinggangshan University)","award":["42061055"],"award-info":[{"award-number":["42061055"]}]},{"name":"Jiangxi Province Key Laboratory of Electronic Data Control and Forensics (Jinggangshan University)","award":["61862035"],"award-info":[{"award-number":["61862035"]}]},{"name":"Jiangxi Province Key Laboratory of Electronic Data Control and Forensics (Jinggangshan University)","award":["20242BAB20128"],"award-info":[{"award-number":["20242BAB20128"]}]},{"name":"National Natural Science Foundation of China","award":["20242BCC32027"],"award-info":[{"award-number":["20242BCC32027"]}]},{"name":"National Natural Science Foundation of China","award":["42061055"],"award-info":[{"award-number":["42061055"]}]},{"name":"National Natural Science Foundation of China","award":["61862035"],"award-info":[{"award-number":["61862035"]}]},{"name":"National Natural Science Foundation of China","award":["20242BAB20128"],"award-info":[{"award-number":["20242BAB20128"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["20242BCC32027"],"award-info":[{"award-number":["20242BCC32027"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["42061055"],"award-info":[{"award-number":["42061055"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["61862035"],"award-info":[{"award-number":["61862035"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["20242BAB20128"],"award-info":[{"award-number":["20242BAB20128"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aiming to solve the challenges of difficult training, mode collapse in current generative adversarial networks (GANs), and the efficiency issue of requiring multiple samples for Denoising Diffusion Probabilistic Models (DDPM), this paper proposes a satellite remote sensing grayscale image colorization method using a denoising GAN. Firstly, a denoising optimization method based on U-ViT for the generator network is introduced to further enhance the model\u2019s generation capability, along with two optimization strategies to significantly reduce the computational burden. Secondly, the discriminator network is optimized by proposing a feature statistical discrimination network, which imposes fewer constraints on the generator network. Finally, grayscale image colorization comparative experiments are conducted on three real satellite remote sensing grayscale image datasets. The results compared with existing typical colorization methods demonstrate that the proposed method can generate color images of higher quality, achieving better performance in both subjective human visual perception and objective metric evaluation. Experiments in building object detection show that the generated color images can improve target detection performance compared to the original grayscale images, demonstrating significant practical value.<\/jats:p>","DOI":"10.3390\/rs16193644","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T05:45:27Z","timestamp":1727675127000},"page":"3644","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Satellite Remote Sensing Grayscale Image Colorization Based on Denoising Generative Adversarial Network"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8561-3246","authenticated-orcid":false,"given":"Qing","family":"Fu","sequence":"first","affiliation":[{"name":"Jiangxi Province Key Laboratory of Electronic Data Control and Forensics, Jinggangshan University, Ji\u2019an 343009, China"},{"name":"Riemann Lab, Huawei Technologies, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyuan","family":"Xia","sequence":"additional","affiliation":[{"name":"Riemann Lab, Huawei Technologies, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifei","family":"Kang","sequence":"additional","affiliation":[{"name":"Riemann Lab, Huawei Technologies, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingwei","family":"Sun","sequence":"additional","affiliation":[{"name":"Riemann Lab, Huawei Technologies, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Tan","sequence":"additional","affiliation":[{"name":"Riemann Lab, Huawei Technologies, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1111\/phor.12339","article-title":"Optimal selection of virtual control points with planar constraints for large-scale block adjustment of satellite imagery","volume":"35","author":"Tong","year":"2020","journal-title":"Photogramm. Rec."},{"key":"ref_2","first-page":"211","article-title":"A GPU-accelerated PCG method for the block adjustment of large-scale high-resolution optical satellite imagery without GCPs. Photogramm","volume":"89","author":"Fu","year":"2023","journal-title":"Eng. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.isprsjprs.2020.11.001","article-title":"A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery","volume":"171","author":"Javan","year":"2021","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.inffus.2020.04.006","article-title":"Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion","volume":"62","author":"Ma","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"10227","DOI":"10.1109\/TGRS.2020.3042974","article-title":"PSGAN: A generative adversarial network for remote sensing image pan-sharpening","volume":"59","author":"Liu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1109\/JSTARS.2013.2283236","article-title":"Two-step sparse coding for the pan-sharpening of remote sensing images","volume":"7","author":"Jiang","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, F.M., Ma, L., and Cai, J. (2018, January 22\u201327). Multi-discriminator generative adversarial network for high resolution gray-scale satellite image colorization. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517930"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5407915","DOI":"10.1109\/TGRS.2022.3154435","article-title":"A deep multitask convolutional neural network for remote sensing image super-resolution and colorization","volume":"60","author":"Feng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jvcir.2018.02.016","article-title":"Single satellite imagery simultaneous super-resolution and colorization using multi-task deep neural networks","volume":"53","author":"Liu","year":"2018","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2899","DOI":"10.1109\/JSTARS.2020.2992082","article-title":"Deep learning for automatic colorization of legacy grayscale aerial photographs","volume":"13","author":"Poterek","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/38.946629","article-title":"Color transfer between images","volume":"21","author":"Reinhard","year":"2001","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Welsh, T., Ashikhmin, M., and Mueller, K. (2002, January 23\u201326). Transferring color to greyscale images. Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, San Antonio, TX, USA.","DOI":"10.1145\/566570.566576"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zong, G.G., Chen, Y., Cao, G.C., and Dong, J.W. (2015, January 12\u201313). Fast image colorization based on local and global consistency. Proceedings of the 2015 8th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.","DOI":"10.1109\/ISCID.2015.128"},{"key":"ref_14","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. arXiv."},{"key":"ref_15","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_16","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Nazeri, K., Ng, E., and Ebrahimi, M. (2018, January 12\u201313). Image colorization using generative adversarial networks. Proceedings of the Articulated Motion and Deformable Objects: 10th International Conference, AMDO 2018, Palma de Mallorca, Spain.","DOI":"10.1007\/978-3-319-94544-6_9"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, M., Jin, X., Jiang, Q., Lee, S.J., Guo, L., Di, Y.D., Huang, S.S., and Huang, J.F. (2019, January 19\u201321). Remote sensing image colorization based on multiscale SEnet GAN. Proceedings of the 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Suzhou, China.","DOI":"10.1109\/CISP-BMEI48845.2019.8965902"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1007\/s00371-020-01933-2","article-title":"Remote sensing image colorization using symmetrical multi-scale DCGAN in YUV color space","volume":"37","author":"Wu","year":"2021","journal-title":"Visual Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, J.Y., Nie, J., Chen, H., Xie, H., Zheng, C.Y., Ye, M., and Wei, Z.Q. (2022, January 13\u201316). Remote sensing image colorization based on joint stream deep convolutional generative adversarial networks. Proceedings of the 4th ACM International Conference on Multimedia in Asia, Tokyo, Japan.","DOI":"10.1145\/3551626.3564951"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5400614","DOI":"10.1109\/TGRS.2023.3342449","article-title":"Bidirectional Layout-Semantic-Pixel Joint Decoupling and Embedding Network for Remote Sensing Colorization","volume":"62","author":"Nie","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","unstructured":"Berthelot, D., Schumm, T., and Metz, L. (2017). Began: Boundary equilibrium generative adversarial networks. arXiv."},{"key":"ref_24","unstructured":"Kodali, N., Abernethy, J., Hays, J., and Kira, Z. (2017). On convergence and stability of gans. arXiv."},{"key":"ref_25","unstructured":"Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y. (2018). Spectral normalization for generative adversarial networks. arXiv."},{"key":"ref_26","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein GAN. arXiv."},{"key":"ref_27","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017). Improved Training of Wasserstein GANs. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mao, X.D., Li, Q., Xie, H.R., Lau, R.Y., Wang, Z., and Paul Smolley, S. (2017, January 22\u201329). Least squares generative adversarial networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref_29","unstructured":"Dhariwal, P., and Nichol, A. (2021). Diffusion Models Beat GANs on Image Synthesis. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022, January 19\u201324). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref_31","unstructured":"Ho, J., Jain, A., and Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. arXiv."},{"key":"ref_32","unstructured":"Sasaki, H., Willcocks, C.G., and Breckon, T.P. (2021). Unit-ddpm: Unpaired image translation with denoising diffusion probabilistic models. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Saharia, C., Chan, W., Chang, H.W., Lee, C., Ho, J., Salimans, T., Fleet, D.J., and Norouzi, M. (2022, January 7\u201311). Palette: Image-to-Image Diffusion Models. Proceedings of the ACM SIGGRAPH 2022 Conference Proceedings, New York, NY, USA.","DOI":"10.1145\/3528233.3530757"},{"key":"ref_34","unstructured":"Song, J.M., Meng, C.L., and Ermon, S. (2020). Denoising diffusion implicit models. arXiv."},{"key":"ref_35","unstructured":"Xiao, Z.S., Kreis, K., and Vahdat, A. (2021). Tackling the generative learning trilemma with denoising diffusion gans. arXiv."},{"key":"ref_36","unstructured":"Wang, Z.D., Zheng, H.J., He, P.C., Chen, W.Z., and Zhou, M.Y. (2022). Diffusion-gan: Training gans with diffusion. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Solano-Carrillo, E., Rodriguez, A.B., Carrillo-Perez, B., Steiniger, Y., and Stoppe, J. (2023, January 17\u201324). Look ATME: The Discriminator Mean Entropy Needs Attention. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00086"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., Ren, S.Q., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bao, F., Nie, S., Xue, K.W., Cao, Y., Li, C.X., Su, H., and Zhu, J. (2023, January 17\u201324). All are worth words: A vit backbone for diffusion models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02171"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Peebles, W., and Xie, S.N. (2023, January 1\u20136). Scalable diffusion models with transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00387"},{"key":"ref_41","unstructured":"El-Nouby, A., Touvron, H., Caron, M., Bojanowski, P., Douze, M., Joulin, A., Laptev, I., Neverova, N., Synnaeve, G., and Verbeek, J. (2021). XCiT: Cross-Covariance Image Transformers. arXiv."},{"key":"ref_42","unstructured":"Veness, J., Lattimore, T., Budden, D., Bhoopchand, A., Mattern, C., Grabska-Barwinska, A., Sezener, E., Wang, J., Toth, P., and Schmitt, S. (2019). Gated Linear Networks. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med. 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