{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T19:05:31Z","timestamp":1761419131208,"version":"build-2065373602"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:00:00Z","timestamp":1759449600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:00:00Z","timestamp":1759449600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306147"],"award-info":[{"award-number":["62306147"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"crossref","award":["BK20241901"],"award-info":[{"award-number":["BK20241901"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Funding for the Support of Individuals Selected for the National Key Talent Programs in Jiangsu Province","award":["R2024PT03"],"award-info":[{"award-number":["R2024PT03"]}]},{"name":"the Startup Foundation for Introducing Talent of NUIST","award":["2024r056"],"award-info":[{"award-number":["2024r056"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11760-025-04828-8","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T18:44:13Z","timestamp":1759517053000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Regression-oriented knowledge distillation for lightweight ship orientation angle prediction with optical remote sensing images"],"prefix":"10.1007","volume":"19","author":[{"given":"Zhan","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ru","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoxuan","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,3]]},"reference":[{"issue":"18","key":"4828_CR1","doi-asserted-by":"publisher","first-page":"2173","DOI":"10.3390\/rs11182173","volume":"11","author":"J Ma","year":"2019","unstructured":"Ma, J., Zhou, Z., Wang, B., Zong, H., Fei, W.: Ship detection in optical satellite images via directional bounding boxes based on ship center and orientation prediction. Remote Sensing 11(18), 2173 (2019)","journal-title":"Remote Sensing"},{"issue":"4","key":"4828_CR2","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1080\/2150704X.2020.1716406","volume":"11","author":"X Niu","year":"2020","unstructured":"Niu, X., Nie, G., Yang, D., Yang, K., Pan, H., Dou, Y., Xia, F.: Learning ship width and direction by convolutional neural networks without manual labelling. Remote Sensing Letters 11(4), 323\u2013332 (2020)","journal-title":"Remote Sensing Letters"},{"key":"4828_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2022.3145790","volume":"19","author":"Y Niu","year":"2022","unstructured":"Niu, Y., Li, Y., Huang, J., Chen, Y.: Efficient encoder-decoder network with estimated direction for sar ship detection. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"9","key":"4828_CR4","doi-asserted-by":"publisher","first-page":"2851","DOI":"10.3390\/s18092851","volume":"18","author":"J Wang","year":"2018","unstructured":"Wang, J., Changhua, L., Jiang, W.: Simultaneous ship detection and orientation estimation in sar images based on attention module and angle regression. Sensors 18(9), 2851 (2018)","journal-title":"Sensors"},{"key":"4828_CR5","doi-asserted-by":"publisher","first-page":"1271","DOI":"10.1109\/JSTARS.2020.2981686","volume":"13","author":"X Zhang","year":"2020","unstructured":"Zhang, X., Lv, Y., Yao, L., Xiong, W., Chunlong, F.: A new benchmark and an attribute-guided multilevel feature representation network for fine-grained ship classification in optical remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, 1271\u20131285 (2020)","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"1","key":"4828_CR6","doi-asserted-by":"publisher","first-page":"132","DOI":"10.3390\/rs10010132","volume":"10","author":"X Yang","year":"2018","unstructured":"Yang, X., Sun, H., Kun, F., Yang, J., Sun, X., Yan, M., Guo, Z.: Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks. Remote sensing 10(1), 132 (2018)","journal-title":"Remote sensing"},{"key":"4828_CR7","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, (2014)"},{"key":"4828_CR8","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. NIPS Deep Learning Workshop, (2015)"},{"issue":"6","key":"4828_CR9","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou, J., Baosheng, Yu., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. Int. J. Comput. Vision 129(6), 1789\u20131819 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"4828_CR10","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNet: Hints for thin deep nets. In International Conference on Learning Representations, (2015)"},{"key":"4828_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124837","volume":"257","author":"C Shi","year":"2024","unstructured":"Shi, C., Hao, Y., Li, G., Shaoyun, X.: Knowledge distillation via noisy feature reconstruction. Expert Syst. Appl. 257, 124837 (2024)","journal-title":"Expert Syst. Appl."},{"key":"4828_CR12","doi-asserted-by":"crossref","unstructured":"Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 3967\u20133976, (2019)","DOI":"10.1109\/CVPR.2019.00409"},{"key":"4828_CR13","unstructured":"Tian, Y., Krishnan, D., Isola, P.: Contrastive representation distillation. In International Conference on Learning Representations, (2019)"},{"key":"4828_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119060","volume":"213","author":"X Ding","year":"2023","unstructured":"Ding, X., Wang, Y., Zuheng, X., Jane Wang, Z., Welch, W.J.: Distilling and transferring knowledge via cgan-generated samples for image classification and regression. Expert Syst. Appl. 213, 119060 (2023)","journal-title":"Expert Syst. Appl."},{"key":"4828_CR15","unstructured":"Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In International Conference on Learning Representations, (2019)"},{"issue":"7","key":"4828_CR16","doi-asserted-by":"publisher","first-page":"8143","DOI":"10.1109\/TPAMI.2022.3228915","volume":"45","author":"X Ding","year":"2023","unstructured":"Ding, X., Wang, Y., Zuheng, X., Welch, W.J., Jane Wang, Z.: Continuous conditional generative adversarial networks: novel empirical losses and label input mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 45(7), 8143\u20138158 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4828_CR17","first-page":"1","volume":"60","author":"X Kejie","year":"2022","unstructured":"Kejie, X., Deng, P., Huang, H.: Vision transformer: an excellent teacher for guiding small networks in remote sensing image scene classification. IEEE Trans. Geosci. Remote Sens. 60, 1\u201315 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4828_CR18","first-page":"1","volume":"19","author":"D Li","year":"2022","unstructured":"Li, D., Nan, Y., Liu, Y.: Remote sensing image scene classification model based on dual knowledge distillation. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"4828_CR19","first-page":"1","volume":"60","author":"Y Yang","year":"2022","unstructured":"Yang, Y., Sun, X., Diao, W., Yin, D., Yang, Z., Li, X.: Statistical sample selection and multivariate knowledge mining for lightweight detectors in remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4828_CR20","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNet V2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4510\u20134520, (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"4828_CR21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770\u2013778, (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"4828_CR22","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. In British Machine Vision Conference 2016, (2016)","DOI":"10.5244\/C.30.87"},{"key":"4828_CR23","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: Shufflenet V2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European Conference on Computer Vision (ECCV), pages 116\u2013131, (2018)","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"4828_CR24","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.neucom.2022.10.070","volume":"517","author":"X Ding","year":"2023","unstructured":"Ding, X., Yongwei Wang, Z., Wang, J., Welch, W.J.: Efficient subsampling of realistic images from gans conditional on a class or a continuous variable. Neurocomputing 517, 188\u2013200 (2023)","journal-title":"Neurocomputing"},{"key":"4828_CR25","first-page":"1","volume":"60","author":"J Chen","year":"2022","unstructured":"Chen, J., Chen, K., Chen, H., Li, W., Zou, Z., Shi, Z.: Contrastive learning for fine-grained ship classification in remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201316 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4828_CR26","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.11834\/jig.200261","volume":"26","author":"L Yao","year":"2021","unstructured":"Yao, L., Zhang, X., Lyu, Y., Sun, W., Li, M.: Fgsc-23: a large-scale dataset of high-resolution optical remote sensing image for deep learning-based fine-grained ship recognition. Image Graph 26, 2337\u20132345 (2021)","journal-title":"Image Graph"},{"issue":"7","key":"4828_CR27","doi-asserted-by":"publisher","first-page":"3108","DOI":"10.1109\/TNNLS.2020.3009523","volume":"32","author":"Q Zhao","year":"2020","unstructured":"Zhao, Q., Dong, J., Hui, Y., Chen, S.: Distilling ordinal relation and dark knowledge for facial age estimation. IEEE Transactions on Neural Networks and Learning Systems 32(7), 3108\u20133121 (2020)","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"4828_CR28","unstructured":"Zhang, H., Goodfellow, I.: Dimitris Metaxas, and Augustus Odena. Self-attention generative adversarial networks. In International Conference on Machine Learning, pages 7354\u20137363, (2019)"},{"key":"4828_CR29","first-page":"1","volume-title":"Dual knowledge distillation for visual tracking with teacher-student network","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Sun, C., Wang, J., Chai, B.: Dual knowledge distillation for visual tracking with teacher-student network, pp. 1\u20139. Signal, Image and Video Processing (2024)"},{"issue":"6","key":"4828_CR30","doi-asserted-by":"publisher","first-page":"1541","DOI":"10.1007\/s11760-021-02108-9","volume":"16","author":"H Ma","year":"2022","unstructured":"Ma, H., Yang, S., Ruowu, W., Hao, X., Long, H., He, G.: Knowledge distillation-based performance transferring for lstm-rnn model acceleration. SIViP 16(6), 1541\u20131548 (2022)","journal-title":"SIViP"},{"issue":"2","key":"4828_CR31","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s11760-021-01922-5","volume":"16","author":"B Li","year":"2022","unstructured":"Li, B., Ji, Y., Li, Y., Yunlong, X., Liu, C.: Pose knowledge transfer for multi-person pose estimation. SIViP 16(2), 321\u2013328 (2022)","journal-title":"SIViP"},{"key":"4828_CR32","first-page":"1","volume-title":"A lightweight road crack detection algorithm based on improved yolov7 model","author":"J He","year":"2024","unstructured":"He, J., Wang, Y., Wang, Y., Li, R., Zhang, D., Zheng, Z.: A lightweight road crack detection algorithm based on improved yolov7 model, pp. 1\u201314. Signal, Image and Video Processing (2024)"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04828-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04828-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04828-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T18:57:19Z","timestamp":1761418639000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04828-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,3]]},"references-count":32,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["4828"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04828-8","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2025,10,3]]},"assertion":[{"value":"12 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1247"}}