{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T07:43:59Z","timestamp":1765439039522,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031781650"},{"type":"electronic","value":"9783031781667"}],"license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-78166-7_21","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T21:35:09Z","timestamp":1733088909000},"page":"320-334","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["PolSAR Image Classification Using Superpixel Profile and\u00a0CNN"],"prefix":"10.1007","author":[{"given":"Nabajyoti","family":"Das","sequence":"first","affiliation":[]},{"given":"Swarnajyoti","family":"Patra","sequence":"additional","affiliation":[]},{"given":"Amos","family":"Bortiew","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"issue":"11","key":"21_CR1","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R Achanta","year":"2012","unstructured":"Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., S\u00fcsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274\u20132282 (2012). https:\/\/doi.org\/10.1109\/TPAMI.2012.120","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"21_CR2","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1109\/LGRS.2018.2799877","volume":"15","author":"SW Chen","year":"2018","unstructured":"Chen, S.W., Tao, C.S.: PolSAR image classification using polarimetric-feature-driven deep convolutional neural network. IEEE Geosci. Remote Sens. Lett. 15(4), 627\u2013631 (2018). https:\/\/doi.org\/10.1109\/LGRS.2018.2799877","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"1","key":"21_CR3","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/36.551935","volume":"35","author":"S Cloude","year":"1997","unstructured":"Cloude, S., Pottier, E.: An entropy based classification scheme for land applications of polarimetric SAR. IEEE Trans. Geosci. Remote Sens. 35(1), 68\u201378 (1997). https:\/\/doi.org\/10.1109\/36.551935","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"21_CR4","doi-asserted-by":"publisher","unstructured":"Das, N., Pradhan, K., Patra, S.: Classification of polarimetric SAR image using JS-divergence profile. In: 2022 IEEE Calcutta Conference (CALCON), pp. 20\u201324 (2022). https:\/\/doi.org\/10.1109\/CALCON56258.2022.10060487","DOI":"10.1109\/CALCON56258.2022.10060487"},{"key":"21_CR5","doi-asserted-by":"publisher","unstructured":"Freeman, A., Durden, S.: A three-component scattering model for polarimetric SAR data. IEEE Trans. Geosci. Remote Sens. 36(3), 963\u2013973 (1998). https:\/\/doi.org\/10.1109\/36.673687","DOI":"10.1109\/36.673687"},{"key":"21_CR6","doi-asserted-by":"publisher","unstructured":"Han, P., Chen, Z., Wan, Y., Cheng, Z.: PoLSAR image classification based on optimal feature and convolution neural network. In: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, pp. 1735\u20131738 (2020). https:\/\/doi.org\/10.1109\/IGARSS39084.2020.9324670","DOI":"10.1109\/IGARSS39084.2020.9324670"},{"key":"21_CR7","doi-asserted-by":"publisher","unstructured":"Hua, W., Wang, X., Zhang, C., Jin, X.: Attention-based multiscale sequential network for PolSAR image classification. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022). https:\/\/doi.org\/10.1109\/LGRS.2022.3164464","DOI":"10.1109\/LGRS.2022.3164464"},{"key":"21_CR8","doi-asserted-by":"publisher","unstructured":"Jamali, A., Mahdianpari, M., Mohammadimanesh, F., Bhattacharya, A., Homayouni, S.: PolSAR image classification based on deep convolutional neural networks using wavelet transformation. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022). https:\/\/doi.org\/10.1109\/LGRS.2022.3185118","DOI":"10.1109\/LGRS.2022.3185118"},{"key":"21_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2023.3239263","volume":"20","author":"A Jamali","year":"2023","unstructured":"Jamali, A., Roy, S.K., Bhattacharya, A., Ghamisi, P.: Local window attention transformer for polarimetric SAR image classification. IEEE Geosci. Remote Sens. Lett. 20, 1\u20135 (2023). https:\/\/doi.org\/10.1109\/LGRS.2023.3239263","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"21_CR10","doi-asserted-by":"publisher","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017). https:\/\/doi.org\/10.1145\/3065386","DOI":"10.1145\/3065386"},{"issue":"2","key":"21_CR11","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s11859-016-1152-y","volume":"21","author":"W Lin","year":"2016","unstructured":"Lin, W., Liao, X., Deng, J., Liu, Y.: Land cover classification of RADARSAT-2 SAR data using convolutional neural network. Wuhan Univ. J. Nat. Sci. 21(2), 151\u2013158 (2016). https:\/\/doi.org\/10.1007\/s11859-016-1152-y","journal-title":"Wuhan Univ. J. Nat. Sci."},{"key":"21_CR12","doi-asserted-by":"publisher","unstructured":"Liu, B., Hu, H., Wang, H., Wang, K., Liu, X., Yu, W.: Superpixel-based classification of polarimetric synthetic aperture radar images. In: 2011 IEEE RadarCon (RADAR), pp. 606\u2013611 (2011). https:\/\/doi.org\/10.1109\/RADAR.2011.5960609","DOI":"10.1109\/RADAR.2011.5960609"},{"key":"21_CR13","doi-asserted-by":"publisher","unstructured":"Liu, M.Y., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy rate superpixel segmentation. In: CVPR 2011, pp. 2097\u20132104 (2011). https:\/\/doi.org\/10.1109\/CVPR.2011.5995323","DOI":"10.1109\/CVPR.2011.5995323"},{"key":"21_CR14","unstructured":"Liu, X., Jiao, L., Liu, F., Hou, X., Zhang, D., Tang, X.: PolSF: PolSAR image dataset on San Francisco. arXiv preprint arXiv:1912.07259 (2019)"},{"key":"21_CR15","unstructured":"Marpu, P.R., Chen, K.S., Chu, C.Y., Benediktsson, J.A.: Spectral-spatial classification of polarimetric SAR data using morphological profiles. In: 2011 3rd International Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), pp.\u00a01\u20133 (2011)"},{"issue":"2","key":"21_CR16","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1109\/TGRS.2015.2469691","volume":"54","author":"A Masjedi","year":"2016","unstructured":"Masjedi, A., Valadan Zoej, M.J., Maghsoudi, Y.: Classification of polarimetric SAR images based on modeling contextual information and using texture features. IEEE Trans. Geosci. Remote Sens. 54(2), 932\u2013943 (2016). https:\/\/doi.org\/10.1109\/TGRS.2015.2469691","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"21_CR17","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1109\/LGRS.2014.2322960","volume":"12","author":"F Qin","year":"2015","unstructured":"Qin, F., Guo, J., Lang, F.: Superpixel segmentation for polarimetric SAR imagery using local iterative clustering. IEEE Geosci. Remote Sens. Lett. 12(1), 13\u201317 (2015). https:\/\/doi.org\/10.1109\/LGRS.2014.2322960","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"4","key":"21_CR18","doi-asserted-by":"publisher","first-page":"2197","DOI":"10.1109\/TGRS.2013.2258675","volume":"52","author":"S Uhlmann","year":"2014","unstructured":"Uhlmann, S., Kiranyaz, S.: Integrating color features in polarimetric SAR image classification. IEEE Trans. Geosci. Remote Sens. 52(4), 2197\u20132216 (2014). https:\/\/doi.org\/10.1109\/TGRS.2013.2258675","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"8","key":"21_CR19","doi-asserted-by":"publisher","first-page":"1780","DOI":"10.1109\/LGRS.2015.2425873","volume":"12","author":"Y Wang","year":"2015","unstructured":"Wang, Y., Liu, H.: PolSAR ship detection based on superpixel-level scattering mechanism distribution features. IEEE Geosci. Remote Sens. Lett. 12(8), 1780\u20131784 (2015). https:\/\/doi.org\/10.1109\/LGRS.2015.2425873","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"8","key":"21_CR20","doi-asserted-by":"publisher","first-page":"1699","DOI":"10.1109\/TGRS.2005.852084","volume":"43","author":"Y Yamaguchi","year":"2005","unstructured":"Yamaguchi, Y., Moriyama, T., Ishido, M., Yamada, H.: Four-component scattering model for polarimetric SAR image decomposition. IEEE Trans. Geosci. Remote Sens. 43(8), 1699\u20131706 (2005). https:\/\/doi.org\/10.1109\/TGRS.2005.852084","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"11","key":"21_CR21","doi-asserted-by":"publisher","first-page":"8796","DOI":"10.1109\/TGRS.2019.2922978","volume":"57","author":"C Yang","year":"2019","unstructured":"Yang, C., Hou, B., Ren, B., Hu, Y., Jiao, L.: CNN-based polarimetric decomposition feature selection for PolSAR image classification. IEEE Trans. Geosci. Remote Sens. 57(11), 8796\u20138812 (2019). https:\/\/doi.org\/10.1109\/TGRS.2019.2922978","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"8","key":"21_CR22","doi-asserted-by":"publisher","first-page":"1440","DOI":"10.1109\/LGRS.2019.2945546","volume":"17","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Liu, K., Dong, Y., Wu, K., Hu, X.: Semisupervised classification based on SLIC segmentation for hyperspectral image. IEEE Geosci. Remote Sens. Lett. 17(8), 1440\u20131444 (2020). https:\/\/doi.org\/10.1109\/LGRS.2019.2945546","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"12","key":"21_CR23","doi-asserted-by":"publisher","first-page":"7177","DOI":"10.1109\/TGRS.2017.2743222","volume":"55","author":"Z Zhang","year":"2017","unstructured":"Zhang, Z., Wang, H., Xu, F., Jin, Y.Q.: Complex-valued convolutional neural network and its application in polarimetric SAR image classification. IEEE Trans. Geosci. Remote Sens. 55(12), 7177\u20137188 (2017). https:\/\/doi.org\/10.1109\/TGRS.2017.2743222","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"21_CR24","doi-asserted-by":"publisher","first-page":"1935","DOI":"10.1109\/LGRS.2016.2618840","volume":"13","author":"Y Zhou","year":"2016","unstructured":"Zhou, Y., Wang, H., Xu, F., Jin, Y.: Polarimetric SAR image classification using deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 13, 1935\u20131939 (2016)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"21_CR25","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1109\/JSTARS.2020.2968966","volume":"13","author":"B Zou","year":"2020","unstructured":"Zou, B., Xu, X., Zhang, L.: Object-based classification of PolSAR images based on spatial and semantic features. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 13, 609\u2013619 (2020). https:\/\/doi.org\/10.1109\/JSTARS.2020.2968966","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78166-7_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T23:37:55Z","timestamp":1733096275000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78166-7_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"ISBN":["9783031781650","9783031781667"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78166-7_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}