{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T06:06:58Z","timestamp":1769580418008,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,15]],"date-time":"2021-10-15T00:00:00Z","timestamp":1634256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971300"],"award-info":[{"award-number":["41971300"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project of Department of Education of Guangdong Province","award":["2020ZDZX3045"],"award-info":[{"award-number":["2020ZDZX3045"]}]},{"name":"Shenzhen Scientific Research and Development Funding Program","award":["JCYJ20180305124802421"],"award-info":[{"award-number":["JCYJ20180305124802421"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,15]]},"DOI":"10.1145\/3497623.3497646","type":"proceedings-article","created":{"date-parts":[[2022,2,5]],"date-time":"2022-02-05T00:30:14Z","timestamp":1644021014000},"page":"142-148","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["A 3D Lightweight Siamese Network for Hyperspectral Image Classification with Limited Samples"],"prefix":"10.1145","author":[{"given":"Shuguo","family":"Jiang","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, China"}]},{"given":"Sen","family":"Jia","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, China and China Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources, Shenzhen University, China"}]}],"member":"320","published-online":{"date-parts":[[2022,2,4]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"S. Amini S. Homayouni and A. Safari. 2014. Semi-supervised classification of hyperspectral image using random forest algorithm. In 2014 IEEE geoscience and remote sensing symposium. IEEE 2866\u20132869.  S. Amini S. Homayouni and A. Safari. 2014. Semi-supervised classification of hyperspectral image using random forest algorithm. In 2014 IEEE geoscience and remote sensing symposium. IEEE 2866\u20132869.","DOI":"10.1109\/IGARSS.2014.6947074"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"X. Cao J. Yao Z. Xu and D. Meng. 2020. Hyperspectral image classification with convolutional neural network and active learning. IEEE Trans. Geosci. Remote Sens.(2020).  X. Cao J. Yao Z. Xu and D. Meng. 2020. Hyperspectral image classification with convolutional neural network and active learning. IEEE Trans. Geosci. Remote Sens.(2020).","DOI":"10.1109\/TGRS.2020.2964627"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2014.2329330"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2010.2091253"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2006.888048"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2015.2436335"},{"key":"e_1_3_2_1_8_1","volume-title":"Summaries of NASA\/JPL Airborne Earth Science Workshop","author":"Foudan S.","unstructured":"S. Foudan , K. Menas , E. Tarek , G. Richard , and Y. Ruixin . 2001. Hyperspectral image analysis for oil spill detection . In Summaries of NASA\/JPL Airborne Earth Science Workshop , Pasadena, CA. 5\u20139. S. Foudan, K. Menas, E. Tarek, G. Richard, and Y. Ruixin. 2001. Hyperspectral image analysis for oil spill detection. In Summaries of NASA\/JPL Airborne Earth Science Workshop, Pasadena, CA. 5\u20139."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.100"},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3279\u20133286","author":"Han X.","unstructured":"X. Han , T. Leung , Y. Jia , R. Sukthankar , and A. Berg . 2015. Matchnet: Unifying feature and metric learning for patch-based matching . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3279\u20133286 . X. Han, T. Leung, Y. Jia, R. Sukthankar, and A. Berg. 2015. Matchnet: Unifying feature and metric learning for patch-based matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3279\u20133286."},{"key":"e_1_3_2_1_11_1","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.770\u2013778","author":"He K.","unstructured":"K. He , X. Zhang , S. Ren , and J. Sun . 2016. Deep residual learning for image recognition . In Proc. IEEE Conf. Comput. Vis. Pattern Recognit.770\u2013778 . K. He, X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit.770\u2013778."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"S. Jia S. Jiang Z. Lin N. Li M. Xu and S. Yu. 2021. A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples. Neurocomputing (2021).  S. Jia S. Jiang Z. Lin N. Li M. Xu and S. Yu. 2021. A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples. Neurocomputing (2021).","DOI":"10.1016\/j.neucom.2021.03.035"},{"key":"e_1_3_2_1_13_1","first-page":"2","article-title":"Gabor Feature-Based Collaborative Representation for Hyperspectral Imagery Classification","volume":"53","author":"Jia S.","year":"2015","unstructured":"S. Jia , L. Shen , and Q. Li . 2015 . Gabor Feature-Based Collaborative Representation for Hyperspectral Imagery Classification . IEEE Transactions on Geoscience and Remote Sensing 53 , 2 (Feb. 2015), 1118\u20131129. S. Jia, L. Shen, and Q. Li. 2015. Gabor Feature-Based Collaborative Representation for Hyperspectral Imagery Classification. IEEE Transactions on Geoscience and Remote Sensing 53, 2 (Feb. 2015), 1118\u20131129.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_2_1_14_1","unstructured":"T. Kipf and M. Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016).  T. Kipf and M. Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/LGRS.2012.2205216","article-title":"Semisupervised hyperspectral image classification using soft sparse multinomial logistic regression. IEEE Geosci","volume":"10","author":"Li J.","year":"2012","unstructured":"J. Li , J. Bioucas-Dias , and A. Plaza . 2012 . Semisupervised hyperspectral image classification using soft sparse multinomial logistic regression. IEEE Geosci . Remote Sens. Lett. 10 , 2 (2012), 318 \u2013 322 . J. Li, J. Bioucas-Dias, and A. Plaza. 2012. Semisupervised hyperspectral image classification using soft sparse multinomial logistic regression. IEEE Geosci. Remote Sens. Lett. 10, 2 (2012), 318\u2013322.","journal-title":"Remote Sens. Lett."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"S. Li W. Song L. Fang Y. Chen P. Ghamisi and J. Benediktsson. 2019. Deep Learning for Hyperspectral Image Classification: An Overview. IEEE Trans Geosci Remote Sens(2019).  S. Li W. Song L. Fang Y. Chen P. Ghamisi and J. Benediktsson. 2019. Deep Learning for Hyperspectral Image Classification: An Overview. IEEE Trans Geosci Remote Sens(2019).","DOI":"10.1109\/TGRS.2019.2907932"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2014.2381602"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs12071054"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2011.2172185"},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 3431\u20133440","author":"Long J.","unstructured":"J. Long , E. Shelhamer , and T. Darrell . 2015. Fully convolutional networks for semantic segmentation . In Proceedings of the IEEE conference on computer vision and pattern recognition. 3431\u20133440 . J. Long, E. Shelhamer, and T. Darrell. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3431\u20133440."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2004.831865"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2010.512425"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"A. Plaza J. Plaza and G. Martin. 2009. Incorporation of spatial constraints into spectral mixture analysis of remotely sensed hyperspectral data. Machine Learning for Signal Processing.mlsp.ieee International Workshop on (2009) 1 \u2013 6.  A. Plaza J. Plaza and G. Martin. 2009. Incorporation of spatial constraints into spectral mixture analysis of remotely sensed hyperspectral data. Machine Learning for Signal Processing.mlsp.ieee International Workshop on (2009) 1 \u2013 6.","DOI":"10.1109\/MLSP.2009.5306202"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2012.2209657"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs12121964"},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 779\u2013788","author":"Redmon J.","unstructured":"J. Redmon , S. Divvala , R. Girshick , and A. Farhadi . 2016. You only look once: Unified, real-time object detection . In Proceedings of the IEEE conference on computer vision and pattern recognition. 779\u2013788 . J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 779\u2013788."},{"key":"e_1_3_2_1_29_1","volume-title":"2014 IEEE Geoscience and Remote Sensing Symposium. IEEE, 2918\u20132921","author":"Sabine C.","unstructured":"C. Sabine , M. Robert , S. Thomas , R. Manuel , E. Paula , P. Marta , and P. Alicia . 2014. Potential of hyperspectral imagery for the spatial assessment of soil erosion stages in agricultural semi-arid Spain at different scales . In 2014 IEEE Geoscience and Remote Sensing Symposium. IEEE, 2918\u20132921 . C. Sabine, M. Robert, S. Thomas, R. Manuel, E. Paula, P. Marta, and P. Alicia. 2014. Potential of hyperspectral imagery for the spatial assessment of soil erosion stages in agricultural semi-arid Spain at different scales. In 2014 IEEE Geoscience and Remote Sensing Symposium. IEEE, 2918\u20132921."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"B. Shi X. Bai and C. Yao. 2016. An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE transactions on pattern analysis and machine intelligence 39 11(2016) 2298\u20132304.  B. Shi X. Bai and C. Yao. 2016. An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE transactions on pattern analysis and machine intelligence 39 11(2016) 2298\u20132304.","DOI":"10.1109\/TPAMI.2016.2646371"},{"key":"e_1_3_2_1_31_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 1\u20139.","author":"Szegedy C.","unstructured":"C. Szegedy , W. Liu , Y. Jia , P. Sermanet , S. Reed , D. Anguelov , D. Erhan , V. Vanhoucke , and A. Rabinovich . 2015. Going deeper with convolutions . In Proceedings of the IEEE conference on computer vision and pattern recognition. 1\u20139. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1\u20139."},{"key":"e_1_3_2_1_32_1","volume-title":"2013 6th International Conference on Recent Advances in Space Technologies (RAST). IEEE, 171\u2013176","author":"Teke M.","unstructured":"M. Teke , H. Deveci , O. Halilo\u011flu , S. G\u00fcrb\u00fcz , and U. Sakarya . 2013. A short survey of hyperspectral remote sensing applications in agriculture . In 2013 6th International Conference on Recent Advances in Space Technologies (RAST). IEEE, 171\u2013176 . M. Teke, H. Deveci, O. Halilo\u011flu, S. G\u00fcrb\u00fcz, and U. Sakarya. 2013. A short survey of hyperspectral remote sensing applications in agriculture. In 2013 6th International Conference on Recent Advances in Space Technologies (RAST). IEEE, 171\u2013176."},{"key":"e_1_3_2_1_33_1","volume-title":"Proc. Geoscience and Remote Sensing Symp.,2009 IEEE Int.,IGARSS 2009","author":"Villa A.","year":"2009","unstructured":"A. Villa , J. Chanussot , C. Jutten , J. Benediktsson , and S. Moussaoui . 2009. On the use of ICA for hyperspectral image analysis . In Proc. Geoscience and Remote Sensing Symp.,2009 IEEE Int.,IGARSS 2009 , Vol.\u00a04. IV\u201397. https:\/\/doi.org\/10.1109\/IGARSS. 2009 .5417363 A. Villa, J. Chanussot, C. Jutten, J. Benediktsson, and S. Moussaoui. 2009. On the use of ICA for hyperspectral image analysis. In Proc. Geoscience and Remote Sensing Symp.,2009 IEEE Int.,IGARSS 2009, Vol.\u00a04. IV\u201397. https:\/\/doi.org\/10.1109\/IGARSS.2009.5417363"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2698503"},{"key":"e_1_3_2_1_35_1","unstructured":"F. Yin Y. Wu X. Zhang and C. Liu. 2017. Scene text recognition with sliding convolutional character models. arXiv preprint arXiv:1709.01727(2017).  F. Yin Y. Wu X. Zhang and C. Liu. 2017. Scene text recognition with sliding convolutional character models. arXiv preprint arXiv:1709.01727(2017)."},{"key":"e_1_3_2_1_36_1","unstructured":"F. Yu and V. Koltun. 2015. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122(2015).  F. Yu and V. Koltun. 2015. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122(2015)."},{"key":"e_1_3_2_1_37_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 4353\u20134361","author":"Zagoruyko S.","unstructured":"S. Zagoruyko and N. Komodakis . 2015. Learning to compare image patches via convolutional neural networks . In Proceedings of the IEEE conference on computer vision and pattern recognition. 4353\u20134361 . S. Zagoruyko and N. Komodakis. 2015. Learning to compare image patches via convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4353\u20134361."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"C. Zhang and Y. Zheng. 2014. Hyperspectral remote sensing image classification based on combined SVM and LDA. In SPIE Asia Pacific Remote Sensing. 92632P.  C. Zhang and Y. Zheng. 2014. Hyperspectral remote sensing image classification based on combined SVM and LDA. In SPIE Asia Pacific Remote Sensing. 92632P.","DOI":"10.1117\/12.2070688"},{"key":"e_1_3_2_1_39_1","volume-title":"IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2567\u20132570","author":"Zhao S.","unstructured":"S. Zhao , W. Li , Q. Du , and Q. Ran . 2018. Hyperspectral classification based on siamese neural network using spectral-spatial feature . In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2567\u20132570 . S. Zhao, W. Li, Q. Du, and Q. Ran. 2018. Hyperspectral classification based on siamese neural network using spectral-spatial feature. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2567\u20132570."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2011.2162589"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.02.105"}],"event":{"name":"ICCPR '21: 2021 10th International Conference on Computing and Pattern Recognition","location":"Shanghai China","acronym":"ICCPR '21"},"container-title":["2021 10th International Conference on Computing and Pattern Recognition"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3497623.3497646","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3497623.3497646","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:23Z","timestamp":1750182563000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3497623.3497646"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,15]]},"references-count":41,"alternative-id":["10.1145\/3497623.3497646","10.1145\/3497623"],"URL":"https:\/\/doi.org\/10.1145\/3497623.3497646","relation":{},"subject":[],"published":{"date-parts":[[2021,10,15]]},"assertion":[{"value":"2022-02-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}