{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T15:57:04Z","timestamp":1774627024534,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:00:00Z","timestamp":1694390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62175054"],"award-info":[{"award-number":["62175054"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61865005"],"award-info":[{"award-number":["61865005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61762033"],"award-info":[{"award-number":["61762033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["620RC554"],"award-info":[{"award-number":["620RC554"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["617079"],"award-info":[{"award-number":["617079"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021-002"],"award-info":[{"award-number":["2021-002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020WNLOKF001"],"award-info":[{"award-number":["2020WNLOKF001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2015BAH55F04"],"award-info":[{"award-number":["2015BAH55F04"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2015BAH55F01"],"award-info":[{"award-number":["2015BAH55F01"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZDKJ2016015"],"award-info":[{"award-number":["ZDKJ2016015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["KYQD(ZR)1882"],"award-info":[{"award-number":["KYQD(ZR)1882"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Hainan Province","award":["62175054"],"award-info":[{"award-number":["62175054"]}]},{"name":"Natural Science Foundation of Hainan Province","award":["61865005"],"award-info":[{"award-number":["61865005"]}]},{"name":"Natural Science Foundation of Hainan Province","award":["61762033"],"award-info":[{"award-number":["61762033"]}]},{"name":"Natural Science Foundation of Hainan Province","award":["620RC554"],"award-info":[{"award-number":["620RC554"]}]},{"name":"Natural Science Foundation of Hainan Province","award":["617079"],"award-info":[{"award-number":["617079"]}]},{"name":"Natural Science Foundation of Hainan Province","award":["2021-002"],"award-info":[{"award-number":["2021-002"]}]},{"name":"Natural Science Foundation of Hainan Province","award":["2020WNLOKF001"],"award-info":[{"award-number":["2020WNLOKF001"]}]},{"name":"Natural Science Foundation of Hainan Province","award":["2015BAH55F04"],"award-info":[{"award-number":["2015BAH55F04"]}]},{"name":"Natural Science Foundation of Hainan Province","award":["2015BAH55F01"],"award-info":[{"award-number":["2015BAH55F01"]}]},{"name":"Natural Science Foundation of Hainan Province","award":["ZDKJ2016015"],"award-info":[{"award-number":["ZDKJ2016015"]}]},{"name":"Natural Science Foundation of Hainan Province","award":["KYQD(ZR)1882"],"award-info":[{"award-number":["KYQD(ZR)1882"]}]},{"name":"Major Science and Technology Project of Haikou City","award":["62175054"],"award-info":[{"award-number":["62175054"]}]},{"name":"Major Science and Technology Project of Haikou City","award":["61865005"],"award-info":[{"award-number":["61865005"]}]},{"name":"Major Science and Technology Project of Haikou City","award":["61762033"],"award-info":[{"award-number":["61762033"]}]},{"name":"Major Science and Technology Project of Haikou City","award":["620RC554"],"award-info":[{"award-number":["620RC554"]}]},{"name":"Major Science and Technology Project of Haikou City","award":["617079"],"award-info":[{"award-number":["617079"]}]},{"name":"Major Science and Technology Project of Haikou City","award":["2021-002"],"award-info":[{"award-number":["2021-002"]}]},{"name":"Major Science and Technology Project of Haikou City","award":["2020WNLOKF001"],"award-info":[{"award-number":["2020WNLOKF001"]}]},{"name":"Major Science and Technology Project of Haikou City","award":["2015BAH55F04"],"award-info":[{"award-number":["2015BAH55F04"]}]},{"name":"Major Science and Technology Project of Haikou City","award":["2015BAH55F01"],"award-info":[{"award-number":["2015BAH55F01"]}]},{"name":"Major Science and Technology Project of Haikou City","award":["ZDKJ2016015"],"award-info":[{"award-number":["ZDKJ2016015"]}]},{"name":"Major Science and Technology Project of Haikou City","award":["KYQD(ZR)1882"],"award-info":[{"award-number":["KYQD(ZR)1882"]}]},{"name":"Wuhan National Laboratory for Optoelectronics","award":["62175054"],"award-info":[{"award-number":["62175054"]}]},{"name":"Wuhan National Laboratory for Optoelectronics","award":["61865005"],"award-info":[{"award-number":["61865005"]}]},{"name":"Wuhan National Laboratory for Optoelectronics","award":["61762033"],"award-info":[{"award-number":["61762033"]}]},{"name":"Wuhan National Laboratory for Optoelectronics","award":["620RC554"],"award-info":[{"award-number":["620RC554"]}]},{"name":"Wuhan National Laboratory for Optoelectronics","award":["617079"],"award-info":[{"award-number":["617079"]}]},{"name":"Wuhan National Laboratory for Optoelectronics","award":["2021-002"],"award-info":[{"award-number":["2021-002"]}]},{"name":"Wuhan National Laboratory for Optoelectronics","award":["2020WNLOKF001"],"award-info":[{"award-number":["2020WNLOKF001"]}]},{"name":"Wuhan National Laboratory for Optoelectronics","award":["2015BAH55F04"],"award-info":[{"award-number":["2015BAH55F04"]}]},{"name":"Wuhan National Laboratory for Optoelectronics","award":["2015BAH55F01"],"award-info":[{"award-number":["2015BAH55F01"]}]},{"name":"Wuhan National Laboratory for Optoelectronics","award":["ZDKJ2016015"],"award-info":[{"award-number":["ZDKJ2016015"]}]},{"name":"Wuhan National Laboratory for Optoelectronics","award":["KYQD(ZR)1882"],"award-info":[{"award-number":["KYQD(ZR)1882"]}]},{"name":"National Key Technology Support Program","award":["62175054"],"award-info":[{"award-number":["62175054"]}]},{"name":"National Key Technology Support Program","award":["61865005"],"award-info":[{"award-number":["61865005"]}]},{"name":"National Key Technology Support Program","award":["61762033"],"award-info":[{"award-number":["61762033"]}]},{"name":"National Key Technology Support Program","award":["620RC554"],"award-info":[{"award-number":["620RC554"]}]},{"name":"National Key Technology Support Program","award":["617079"],"award-info":[{"award-number":["617079"]}]},{"name":"National Key Technology Support Program","award":["2021-002"],"award-info":[{"award-number":["2021-002"]}]},{"name":"National Key Technology Support Program","award":["2020WNLOKF001"],"award-info":[{"award-number":["2020WNLOKF001"]}]},{"name":"National Key Technology Support Program","award":["2015BAH55F04"],"award-info":[{"award-number":["2015BAH55F04"]}]},{"name":"National Key Technology Support Program","award":["2015BAH55F01"],"award-info":[{"award-number":["2015BAH55F01"]}]},{"name":"National Key Technology Support Program","award":["ZDKJ2016015"],"award-info":[{"award-number":["ZDKJ2016015"]}]},{"name":"National Key Technology Support Program","award":["KYQD(ZR)1882"],"award-info":[{"award-number":["KYQD(ZR)1882"]}]},{"name":"Major Science and Technology Project of Hainan Province","award":["62175054"],"award-info":[{"award-number":["62175054"]}]},{"name":"Major Science and Technology Project of Hainan Province","award":["61865005"],"award-info":[{"award-number":["61865005"]}]},{"name":"Major Science and Technology Project of Hainan Province","award":["61762033"],"award-info":[{"award-number":["61762033"]}]},{"name":"Major Science and Technology Project of Hainan Province","award":["620RC554"],"award-info":[{"award-number":["620RC554"]}]},{"name":"Major Science and Technology Project of Hainan Province","award":["617079"],"award-info":[{"award-number":["617079"]}]},{"name":"Major Science and Technology Project of Hainan Province","award":["2021-002"],"award-info":[{"award-number":["2021-002"]}]},{"name":"Major Science and Technology Project of Hainan Province","award":["2020WNLOKF001"],"award-info":[{"award-number":["2020WNLOKF001"]}]},{"name":"Major Science and Technology Project of Hainan Province","award":["2015BAH55F04"],"award-info":[{"award-number":["2015BAH55F04"]}]},{"name":"Major Science and Technology Project of Hainan Province","award":["2015BAH55F01"],"award-info":[{"award-number":["2015BAH55F01"]}]},{"name":"Major Science and Technology Project of Hainan Province","award":["ZDKJ2016015"],"award-info":[{"award-number":["ZDKJ2016015"]}]},{"name":"Major Science and Technology Project of Hainan Province","award":["KYQD(ZR)1882"],"award-info":[{"award-number":["KYQD(ZR)1882"]}]},{"name":"Scientific Research Staring Foundation of Hainan University","award":["62175054"],"award-info":[{"award-number":["62175054"]}]},{"name":"Scientific Research Staring Foundation of Hainan University","award":["61865005"],"award-info":[{"award-number":["61865005"]}]},{"name":"Scientific Research Staring Foundation of Hainan University","award":["61762033"],"award-info":[{"award-number":["61762033"]}]},{"name":"Scientific Research Staring Foundation of Hainan University","award":["620RC554"],"award-info":[{"award-number":["620RC554"]}]},{"name":"Scientific Research Staring Foundation of Hainan University","award":["617079"],"award-info":[{"award-number":["617079"]}]},{"name":"Scientific Research Staring Foundation of Hainan University","award":["2021-002"],"award-info":[{"award-number":["2021-002"]}]},{"name":"Scientific Research Staring Foundation of Hainan University","award":["2020WNLOKF001"],"award-info":[{"award-number":["2020WNLOKF001"]}]},{"name":"Scientific Research Staring Foundation of Hainan University","award":["2015BAH55F04"],"award-info":[{"award-number":["2015BAH55F04"]}]},{"name":"Scientific Research Staring Foundation of Hainan University","award":["2015BAH55F01"],"award-info":[{"award-number":["2015BAH55F01"]}]},{"name":"Scientific Research Staring Foundation of Hainan University","award":["ZDKJ2016015"],"award-info":[{"award-number":["ZDKJ2016015"]}]},{"name":"Scientific Research Staring Foundation of Hainan University","award":["KYQD(ZR)1882"],"award-info":[{"award-number":["KYQD(ZR)1882"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Infrared and visible image fusion is a solution that generates an information-rich individual image with different modal information by fusing images obtained from various sensors. Salient detection can better emphasize the targets of concern. We propose a residual Swin Transformer fusion network based on saliency detection, termed SDRSwin, aiming to highlight the salient thermal targets in the infrared image while maintaining the texture details in the visible image. The SDRSwin network is trained with a two-stage training approach. In the first stage, we train an encoder\u2013decoder network based on residual Swin Transformers to achieve powerful feature extraction and reconstruction capabilities. In the second stage, we develop a novel salient loss function to guide the network to fuse the salient targets in the infrared image and the background detail regions in the visible image. The extensive results indicate that our method has abundant texture details with clear bright infrared targets and achieves a better performance than the twenty-one state-of-the-art methods in both subjective and objective evaluation.<\/jats:p>","DOI":"10.3390\/rs15184467","type":"journal-article","created":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T03:54:06Z","timestamp":1694490846000},"page":"4467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["SDRSwin: A Residual Swin Transformer Network with Saliency Detection for Infrared and Visible Image Fusion"],"prefix":"10.3390","volume":"15","author":[{"given":"Shengshi","family":"Li","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5458-9509","authenticated-orcid":false,"given":"Guanjun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Hainan University, Haikou 570228, China"},{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Genetics and Germplasm Innovation of Tropical Special Forest Trees and Ornamental Plants (Hainan University), Ministry of Education, School of Forestry, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonghua","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Hainan University, Haikou 570228, China"},{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Qi, B., Jin, L., Li, G., Zhang, Y., Li, Q., Bi, G., and Wang, W. (2022). Infrared and Visible Image Fusion Based on Co-Occurrence Analysis Shearlet Transform. Remote Sens., 14.","DOI":"10.3390\/rs14020283"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2913","DOI":"10.1109\/TCSVT.2018.2874312","article-title":"Learning local-global multi-graph descriptors for RGB-T object tracking","volume":"29","author":"Li","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106977","DOI":"10.1016\/j.patcog.2019.106977","article-title":"RGB-T object tracking: Benchmark and baseline","volume":"96","author":"Li","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.infrared.2019.04.017","article-title":"Thermal infrared and visible sequences fusion tracking based on a hybrid tracking framework with adaptive weighting scheme","volume":"99","author":"Luo","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"10385","DOI":"10.1038\/s41598-023-37295-7","article-title":"Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys","volume":"13","author":"Krishnan","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/0167-8655(89)90003-2","article-title":"Image fusion by a ratio of low-pass pyramid","volume":"9","author":"Toet","year":"1989","journal-title":"Pattern Recognit. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1006\/gmip.1995.1022","article-title":"Multisensor image fusion using the wavelet transform","volume":"57","author":"Li","year":"1995","journal-title":"Graph. Model. Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.inffus.2006.02.001","article-title":"Remote sensing image fusion using the curvelet transform","volume":"8","author":"Nencini","year":"2007","journal-title":"Inf. Fusion"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.inffus.2005.09.006","article-title":"Pixel-and region-based image fusion with complex wavelets","volume":"8","author":"Lewis","year":"2007","journal-title":"Inf. Fusion"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"479","DOI":"10.14429\/dsj.61.705","article-title":"Image fusion technique using multi-resolution singular value decomposition","volume":"61","author":"Naidu","year":"2011","journal-title":"Def. Sci. J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, H., and Wu, X.J. (2018). Infrared and visible image fusion using latent low-rank representation. arXiv.","DOI":"10.1109\/ICPR.2018.8546006"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3064","DOI":"10.1364\/AO.58.003064","article-title":"Infrared and visible image perceptive fusion through multi-level Gaussian curvature filtering image decomposition","volume":"58","author":"Tan","year":"2019","journal-title":"Appl. Opt."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"057006","DOI":"10.1117\/1.OE.52.5.057006","article-title":"Dictionary learning method for joint sparse representation-based image fusion","volume":"52","author":"Zhang","year":"2013","journal-title":"Opt. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.ijleo.2016.09.126","article-title":"Texture clear multi-modal image fusion with joint sparsity model","volume":"130","author":"Gao","year":"2017","journal-title":"Optik"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.infrared.2016.01.009","article-title":"Two-scale image fusion of visible and infrared images using saliency detection","volume":"76","author":"Bavirisetti","year":"2016","journal-title":"Infrared Phys. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.infrared.2017.02.005","article-title":"Infrared and visible image fusion based on visual saliency map and weighted least square optimization","volume":"82","author":"Ma","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1109\/TCI.2021.3100986","article-title":"Classification saliency-based rule for visible and infrared image fusion","volume":"7","author":"Xu","year":"2021","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.inffus.2016.02.001","article-title":"Infrared and visible image fusion via gradient transfer and total variation minimization","volume":"31","author":"Ma","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Du, Q., Xu, H., Ma, Y., Huang, J., and Fan, F. (2018). Fusing infrared and visible images of different resolutions via total variation model. Sensors, 18.","DOI":"10.3390\/s18113827"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., and Timofte, R. (2021, January 11\u201317). Swinir: Image restoration using swin transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, Z., Chen, Y., Shao, W., Li, H., and Zhang, L. (2022). SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images. arXiv.","DOI":"10.1109\/TIM.2022.3191664"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.inffus.2021.02.023","article-title":"RFN-Nest: An end-to-end residual fusion network for infrared and visible images","volume":"73","author":"Li","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.inffus.2018.09.004","article-title":"FusionGAN: A generative adversarial network for infrared and visible image fusion","volume":"48","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, H., Wu, X.J., and Kittler, J. (2018, January 20\u201324). Infrared and visible image fusion using a deep learning framework. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8546006"},{"key":"ref_25","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103039","DOI":"10.1016\/j.infrared.2019.103039","article-title":"Infrared and visible image fusion with ResNet and zero-phase component analysis","volume":"102","author":"Li","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, S., Zou, Y., Wang, G., and Lin, C. (2023). Infrared and Visible Image Fusion Method Based on a Principal Component Analysis Network and Image Pyramid. Remote Sens., 15.","DOI":"10.3390\/rs15030685"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","article-title":"DenseFuse: A fusion approach to infrared and visible images","volume":"28","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","first-page":"1","article-title":"DRF: Disentangled representation for visible and infrared image fusion","volume":"70","author":"Xu","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_30","first-page":"1","article-title":"GANMcC: A generative adversarial network with multiclassification constraints for infrared and visible image fusion","volume":"70","author":"Ma","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","article-title":"U2Fusion: A unified unsupervised image fusion network","volume":"44","author":"Xu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Advances in Neural Information Processing Systems 30 (NIPS 2017), NeurIPS."},{"key":"ref_33","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2022, January 23\u201327). Swin-unet: Unet-like pure transformer for medical image segmentation. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"ref_36","unstructured":"Lin, L., Fan, H., Xu, Y., and Ling, H. (2021). Swintrack: A simple and strong baseline for transformer tracking. arXiv."},{"key":"ref_37","unstructured":"Toet, A. (2023, June 01). TNO Image Fusion Dataset. Available online: https:\/\/figshare.com\/articles\/TN_Image_Fusion_Dataset\/1008029."},{"key":"ref_38","first-page":"12484","article-title":"Fusiondn: A unified densely connected network for image fusion","volume":"34","author":"Xu","year":"2020","journal-title":"Aaai Conf. Artif. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, H., Turvey, S.T., Pandey, S.P., Song, X., Sun, Z., and Wang, N. (2023). Commercial drones can provide accurate and effective monitoring of the world\u2019s rarest primate. Remote. Sens. Ecol. Conserv.","DOI":"10.1002\/rse2.341"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e02250","DOI":"10.1016\/j.gecco.2022.e02250","article-title":"Automatic detection for the world\u2019s rarest primates based on a tropical rainforest environment","volume":"38","author":"Wang","year":"2022","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_41","unstructured":"IUCN (2023, June 01). The IUCN Red List of Threatened Species. Available online: http:\/\/www.iucnredlist.org."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e1600946","DOI":"10.1126\/sciadv.1600946","article-title":"Impending extinction crisis of the world\u2019s primates: Why primates matter","volume":"3","author":"Estrada","year":"2017","journal-title":"Sci. Adv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e01101","DOI":"10.1016\/j.gecco.2020.e01101","article-title":"Thermal infrared imaging from drones can detect individuals and nocturnal behavior of the world\u2019s rarest primate","volume":"23","author":"Zhang","year":"2020","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hwang, S., Park, J., Kim, N., Choi, Y., and So Kweon, I. (2015, January 7\u201312). Multispectral pedestrian detection: Benchmark dataset and baseline. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298706"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"023522","DOI":"10.1117\/1.2945910","article-title":"Assessment of image fusion procedures using entropy, image quality, and multispectral classification","volume":"2","author":"Roberts","year":"2008","journal-title":"J. Appl. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1088\/0957-0233\/8\/4\/002","article-title":"In-fibre Bragg grating sensors","volume":"8","author":"Rao","year":"1997","journal-title":"Meas. Sci. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1049\/el:20081754","article-title":"Comments on \u2018Information measure for performance of image fusion\u2019","volume":"44","author":"Hossny","year":"2008","journal-title":"Electron. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/B978-0-12-372529-5.00017-2","article-title":"Performance evaluation of image fusion techniques","volume":"19","author":"Wang","year":"2008","journal-title":"Image Fusion Algorithms Appl."},{"key":"ref_53","first-page":"1433","article-title":"Performance assessment of combinative pixel-level image fusion based on an absolute feature measurement","volume":"3","author":"Zhao","year":"2007","journal-title":"Int. J. Innov. Comput. Inf. Control"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.inffus.2005.10.001","article-title":"A human perception inspired quality metric for image fusion based on regional information","volume":"8","author":"Chen","year":"2007","journal-title":"Inf. Fusion"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TIP.2005.859378","article-title":"Image information and visual quality","volume":"15","author":"Sheikh","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1049\/el:20020212","article-title":"Information measure for performance of image fusion","volume":"38","author":"Qu","year":"2002","journal-title":"Electron. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4467\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:49:00Z","timestamp":1760129340000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,11]]},"references-count":56,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15184467"],"URL":"https:\/\/doi.org\/10.3390\/rs15184467","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,11]]}}}