{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:27:49Z","timestamp":1760149669252,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T00:00:00Z","timestamp":1694044800000},"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":["62005049","2020J01451","JAT190003"],"award-info":[{"award-number":["62005049","2020J01451","JAT190003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["62005049","2020J01451","JAT190003"],"award-info":[{"award-number":["62005049","2020J01451","JAT190003"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Education and Scientific Research Foundation for Young Teachers in Fujian Province","award":["62005049","2020J01451","JAT190003"],"award-info":[{"award-number":["62005049","2020J01451","JAT190003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The fusion of spectral\u2013polarimetric information can improve the autonomous reconnaissance capability of unmanned aerial vehicles (UAVs) in detecting artificial targets. However, the current spectral and polarization imaging systems typically suffer from low image sampling resolution, which can lead to the loss of target information. Most existing segmentation algorithms neglect the similarities and differences between multimodal features, resulting in reduced accuracy and robustness of the algorithms. To address these challenges, a real-time spectral\u2013polarimetric segmentation algorithm for artificial targets based on an efficient attention fusion network, called ESPFNet (efficient spectral\u2013polarimetric fusion network) is proposed. The network employs a coordination attention bimodal fusion (CABF) module and a complex atrous spatial pyramid pooling (CASPP) module to fuse and enhance low-level and high-level features at different scales from the spectral feature images and the polarization encoded images, effectively achieving the segmentation of artificial targets. Additionally, the introduction of the residual dense block (RDB) module refines feature extraction, further enhancing the network\u2019s ability to classify pixels. In order to test the algorithm\u2019s performance, a spectral\u2013polarimetric image dataset of artificial targets, named SPIAO (spectral\u2013polarimetric image of artificial objects) is constructed, which contains various camouflaged nets and camouflaged plates with different properties. The experimental results on the SPIAO dataset demonstrate that the proposed method accurately detects the artificial targets, achieving a mean intersection-over-union (MIoU) of 80.4%, a mean pixel accuracy (MPA) of 88.1%, and a detection rate of 27.5 frames per second, meeting the real-time requirement. The research has the potential to provide a new multimodal detection technique for enabling autonomous reconnaissance by UAVs in complex scenes.<\/jats:p>","DOI":"10.3390\/rs15184398","type":"journal-article","created":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T10:09:50Z","timestamp":1694081390000},"page":"4398","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Real-Time Segmentation of Artificial Targets Using a Dual-Modal Efficient Attention Fusion Network"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1322-1753","authenticated-orcid":false,"given":"Ying","family":"Shen","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}]},{"given":"Xiancai","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}]},{"given":"Shuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}]},{"given":"Yixuan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}]},{"given":"Dawei","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0829-5694","authenticated-orcid":false,"given":"Shu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}]},{"given":"Feng","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.cja.2016.01.012","article-title":"Haze removal for UAV reconnaissance images using layered scattering model","volume":"29","author":"Huang","year":"2016","journal-title":"Chin. J. Aeronaut."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7155","DOI":"10.1007\/s00500-021-05675-8","article-title":"Multi-UAV reconnaissance task allocation for heterogeneous targets using grouping ant colony optimization algorithm","volume":"25","author":"Gao","year":"2021","journal-title":"Soft Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1016\/j.dt.2020.08.007","article-title":"MF-CFI: A fused evaluation index for camouflage patterns based on human visual perception","volume":"17","author":"Yang","year":"2021","journal-title":"Def. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5708","DOI":"10.1109\/TCSVT.2021.3124952","article-title":"Rethinking Camouflaged Object Detection: Models and Datasets","volume":"32","author":"Bi","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2050028","DOI":"10.1142\/S021946782050028X","article-title":"Camouflaged Object Detection and Tracking: A Survey","volume":"20","author":"Mondal","year":"2020","journal-title":"Int. J. Image Graph."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s00530-014-0368-y","article-title":"Camouflage texture evaluation using a saliency map","volume":"21","author":"Feng","year":"2015","journal-title":"Multimed. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1109\/TCSVT.2016.2555719","article-title":"A Bayesian Approach to Camouflaged Moving Object Detection","volume":"27","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_8","first-page":"20130064","article-title":"Camouflage, detection and identification of moving targets","volume":"280","author":"Hall","year":"2013","journal-title":"Proc. Biol. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fan, D., Ji, G., Sun, G., Cheng, M., Shen, J., and Shao, L. (2020, January 13\u201319). Camouflaged Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5364","DOI":"10.1109\/TIE.2021.3078379","article-title":"D2C-Net: A dual-branch, dual-guidance and cross-refine network for camouflaged object detection","volume":"69","author":"Wang","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7036","DOI":"10.1109\/TIP.2022.3217695","article-title":"Feature aggregation and propagation network for camouflaged object detection","volume":"31","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mei, H., Ji, G., Wei, Z., Yang, X., Wei, X., and Fan, D. (2021, January 20\u201325). Camouflaged object segmentation with distraction mining. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00866"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tan, J., Zhang, J., and Zou, B. (2016, January 17\u201321). Camouflage target detection based on polarized spectral features. Proceedings of the SPIE 9853, Polarization: Measurement, Analysis, and Remote Sensing XII, Baltimore, MD, USA.","DOI":"10.1117\/12.2222160"},{"key":"ref_14","first-page":"1","article-title":"Rapid detection of camouflaged artificial target based on polarization imaging and deep learning","volume":"13","author":"Shen","year":"2021","journal-title":"IEEE Photonics J."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shen, Y., Li, J., Lin, W., Chen, L., Huang, F., and Wang, S. (2021). Camouflaged target detection based on snapshot multispectral imaging. Remote Sens., 13.","DOI":"10.3390\/rs13193949"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhou, P.C., and Liu, C.C. (2013, January 21). Camouflaged target separation by spectral-polarimetric imagery fusion with shearlet transform and Clustering Segmentation. Proceedings of the International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Sensors and Applications, Beingjing, China.","DOI":"10.1117\/12.2033944"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Islam, M.N., Tahtali, M., and Pickering, M. (2020). Hybrid fusion-based background segmentation in multispectral polarimetric imagery. Remote Sens., 12.","DOI":"10.3390\/rs12111776"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/LGRS.2017.2671439","article-title":"Target detection for polarized hyperspectral images based on tensor decomposition","volume":"14","author":"Tan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2235","DOI":"10.1109\/LGRS.2017.2758762","article-title":"Joint sparse tensor representation for the target detection of polarized hyperspectral images","volume":"14","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4802","DOI":"10.1364\/OE.416130","article-title":"Polarization-driven semantic segmentation via efficient attention-bridged fusion","volume":"29","author":"Xiang","year":"2021","journal-title":"Opt. Express"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10753","DOI":"10.1109\/TITS.2021.3095658","article-title":"The PolarLITIS dataset: Road scenes under fog","volume":"23","author":"Blin","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_22","first-page":"191","article-title":"Review of spectral and polarization imaging systems","volume":"Volume 11351","author":"Sattar","year":"2020","journal-title":"Proceedings of the Unconventional Optical Imaging II"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"126946","DOI":"10.1016\/j.optcom.2021.126946","article-title":"Compressive circular polarization snapshot spectral imaging","volume":"491","author":"Ning","year":"2021","journal-title":"Opt. Commun."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Son, D., Kwon, H., and Lee, S. (2020). Visible and near-infrared image synthesis using PCA fusion of multiscale layers. Appl. Sci., 10.","DOI":"10.3390\/app10238702"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1364\/OE.27.001376","article-title":"Demosaicking DoFP images using newton\u2019s polynomial interpolation and polarization difference model","volume":"27","author":"Li","year":"2019","journal-title":"Opt. Express"},{"key":"ref_26","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Roy, A.G., Navab, N., and Wachinger, C. (2018, January 16\u201320). Concurrent spatial and channel squeeze & excitation in fully convolutional networks. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Granada, Spain.","DOI":"10.1007\/978-3-030-00928-1_48"},{"key":"ref_30","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_31","first-page":"5910","article-title":"Optimal clustering framework for hyperspectral band selection","volume":"56","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","first-page":"351","article-title":"Sparse nonnegative matrix factorization for hyperspectral optimal band selection","volume":"42","author":"Shi","year":"2013","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1109\/JSTARS.2014.2315772","article-title":"An overview of background modeling for detection of targets and anomalies in hyperspectral remotely sensed imagery","volume":"7","author":"Matteoli","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., and Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, C., Bochkovskiy, A., and Liao, H.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Image Computing and Computer Assisted Intervention (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 16\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_40","unstructured":"Guo, M., Lu, C., Hou, Q., Liu, Z., Cheng, M., and Hu, S. (2022). SegNeXt: Rethinking convolutional attention design for semantic segmentation. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kim, J., Koh, J., Kim, Y., Choi, J., Hwang, Y., and Choi, J.W. (2018, January 2\u20136). Robust deep multi-modal learning based on gated information fusion network. Proceedings of the 2018 Asian Coference on Computer Vision (ACCV), Perth, Australia.","DOI":"10.1007\/978-3-030-20870-7_6"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hu, X., Yang, K., Fei, L., and Wang, K. (2019, January 22\u201325). ACNET: Attention based network to exploit complementary features for rgbd semantic segmentation. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803025"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Seichter, D., K\u00f6hler, M., Lewandowski, B., Wengefeld, T., and Gross, H.M. (June, January 30). Efficient rgb-d semantic segmentation for indoor scene analysis. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561675"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Cao, Z. (2021). C3Net: Cross-modal feature recalibrated, cross-scale semantic aggregated and compact network for semantic segmentation of multi-modal high-resolution aerial images. Remote Sens., 13.","DOI":"10.3390\/rs13030528"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., and Cottrell, G.W. (2018, January 12\u201315). Understanding convolution for semantic seg-mentation. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NA, USA.","DOI":"10.1109\/WACV.2018.00163"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3641","DOI":"10.1109\/TSMC.2019.2957386","article-title":"Global and local-contrast guides content-aware fusion for rgb-d saliency prediction","volume":"51","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4398\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:46:38Z","timestamp":1760129198000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4398"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,7]]},"references-count":46,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15184398"],"URL":"https:\/\/doi.org\/10.3390\/rs15184398","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,9,7]]}}}