{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:07:13Z","timestamp":1786979233202,"version":"build-2736575974"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819512324","type":"print"},{"value":"9789819512331","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-1233-1_48","type":"book-chapter","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T06:06:07Z","timestamp":1755842767000},"page":"535-546","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Hyperspectral Endmember Material Identification Using Spectral Library Matching"],"prefix":"10.1007","author":[{"given":"Nian","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fred","family":"Rischmiller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wagdy H.","family":"Mahmoud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"key":"48_CR1","doi-asserted-by":"publisher","unstructured":"Rochac, J.F.R., Zhang, N., Thompson, L., Deksissa, T.: A robust context-based deep learning approach for highly-imbalanced hyperspectral classification. Comput. Intell. Neurosci. 9923491 (2021). https:\/\/doi.org\/10.1155\/2021\/9923491","DOI":"10.1155\/2021\/9923491"},{"key":"48_CR2","doi-asserted-by":"crossref","unstructured":"Zhang, N., Rouamba, S., Mahmoud, W., Thompson, L.: Hyperspectral image analysis using maximum abundance classification. In: Proceedings of the 13th International Conference on Intelligent Control and Information Processing (ICICIP 2025), Muscat, Oman, 6\u201311 February 2025 (2025)","DOI":"10.1109\/ICICIP64458.2025.10898095"},{"key":"48_CR3","doi-asserted-by":"crossref","unstructured":"Baker-Adell, K., Zhang, N., Denis, M.: Semantic neuroanatomical segmentation of brain MRI scans using pretrained SynthSeg neural network. In: Proceedings of the 13th International Conference on Intelligent Control and Information Processing (ICICIP 2025), Muscat, Oman, 6\u201311 February 2025 (2025)","DOI":"10.1109\/ICICIP64458.2025.10898143"},{"key":"48_CR4","doi-asserted-by":"crossref","unstructured":"Zhang, N., Thompson, L.: Classifying land cover using hyperspectral image and LiDAR data. In: Proceedings of the 13th International Conference on Intelligent Control and Information Processing (ICICIP 2025), Muscat, Oman, 6\u201311 February 2025 (2025)","DOI":"10.1109\/ICICIP64458.2025.10898127"},{"key":"48_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, N., Mahmoud, W.: Target detection in hyperspectral imagery using spectral signature matching algorithms. In: Proceedings of the 13th International Conference on Intelligent Control and Information Processing (ICICIP 2025), Muscat, Oman, 6\u201311 February 2025 (2025)","DOI":"10.1109\/ICICIP64458.2025.10898116"},{"key":"48_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, N., Wilson, O., Thompson, L.: Target identification and detection using hyperspectral signature transformation. In: Proceedings of the 13th International Conference on Intelligent Control and Information Processing (ICICIP 2025), Muscat, Oman, 6\u201311 February 2025 (2025)","DOI":"10.1109\/ICICIP64458.2025.10898093"},{"key":"48_CR7","doi-asserted-by":"crossref","unstructured":"Rouamba, S., Zhang, N., Mahmoud, W.H., Thompson, L., Denis, M., Deksissa, T.: Hyperspectral image classification using custom spectral convolutional neural networks (CSCNNs). In: Proceedings of the 14th International Conference on Information Science and Technology (ICIST 2024), Chengdu, China, 6\u20139 December 2024 (2024)","DOI":"10.1109\/ICIST63249.2024.10805473"},{"key":"48_CR8","unstructured":"Rochac, J.F.R., Thompson, L., Zhang, N., Oladunni, T.: A data augmentation-assisted deep learning model for high dimensional and highly imbalanced hyperspectral imaging data. In: Proceedings of the 9th International Conference on Information Science and Technology (ICIST 2019), Hulunbuir, China, 2\u20135 August 2019 (2019)"},{"key":"48_CR9","doi-asserted-by":"crossref","unstructured":"Rochac, J.F.R., Zhang, N., Behera, P.: Design of adaptive feature extraction algorithm based on fuzzy classifier in hyperspectral imagery classification for big data analysis. In: Proceedings of the 12th World Congress on Intelligent Control and Automation (WCICA 2016), Guilin, China, pp. 1046\u20131051 (2016)","DOI":"10.1109\/WCICA.2016.7578527"},{"key":"48_CR10","doi-asserted-by":"crossref","unstructured":"Rochac, J.F.R., Zhang, N.: Feature extraction in hyperspectral imaging using adaptive feature selection approach. In: Proceedings of the 8th International Conference on Advanced Computational Intelligence (ICACI 2016), Chiang Mai, Thailand, pp. 36\u201340 (2016)","DOI":"10.1109\/ICACI.2016.7449799"},{"key":"48_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3296728","volume":"61","author":"CI Chang","year":"2023","unstructured":"Chang, C.I., Kuo, Y.M., Hu, P.F.: Unsupervised rate distortion function-based band subset selection for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 61, 1\u201318 (2023). https:\/\/doi.org\/10.1109\/TGRS.2023.3296728","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"48_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2023.3238962","volume":"20","author":"A Ert\u00fcrk","year":"2023","unstructured":"Ert\u00fcrk, A., Erten, E.: Unmixing of pollution-associated sea snot in the near surface after its outbreak in the Sea of Marmara using hyperspectral PRISMA data. IEEE Geosci. Remote Sens. Lett. 20, 1\u20135 (2023). https:\/\/doi.org\/10.1109\/LGRS.2023.3238962","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"48_CR13","doi-asserted-by":"publisher","unstructured":"\u00d6zdemir, O.B., Koz, A.: 3D-CNN and autoencoder-based gas detection in hyperspectral images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 16, 1474\u20131482 (2023). https:\/\/doi.org\/10.1109\/JSTARS.2023.3235781","DOI":"10.1109\/JSTARS.2023.3235781"},{"key":"48_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3176913","volume":"60","author":"X He","year":"2022","unstructured":"He, X., Chen, Y., Huang, L.: Toward a trustworthy classifier with deep CNN: uncertainty estimation meets hyperspectral image. IEEE Trans. Geosci. Remote Sens. 60, 1\u201315 (2022). https:\/\/doi.org\/10.1109\/TGRS.2022.3176913","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"48_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, N., Mahmoud, W.H.: Convex geometry based endmember extraction for hyperspectral images classification. In: Proceedings of the 13th International Conference on Information Science and Technology (ICIST 2023), Cairo, Egypt, 8\u201314 December 2023 (2023)","DOI":"10.1109\/ICIST59754.2023.10367140"},{"key":"48_CR16","doi-asserted-by":"publisher","unstructured":"Chang, C.I., Bekit, A.: Endmember finding in compressively sensed band domain. In: Chang, C.I. (eds.) Advances in Hyperspectral Image Processing Techniques. Wiley. https:\/\/doi.org\/10.1002\/9781119687788.ch8","DOI":"10.1002\/9781119687788.ch8"},{"key":"48_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2025.3542614","volume":"63","author":"T Zhang","year":"2025","unstructured":"Zhang, T., et al.: Multibaseline interferometry based on independent component analysis and InSAR combinatorial modeling for high-precision DEM reconstruction. IEEE Trans. Geosci. Remote Sens. 63, 1\u201317 (2025). https:\/\/doi.org\/10.1109\/TGRS.2025.3542614","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"6","key":"48_CR18","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1109\/TIP.2010.2103949","volume":"20","author":"R He","year":"2011","unstructured":"He, R., Hu, B.-G., Zheng, W.-S., Kong, X.-W.: Robust principal component analysis based on maximum correntropy criterion. IEEE Trans. Image Process. 20(6), 1485\u20131494 (2011). https:\/\/doi.org\/10.1109\/TIP.2010.2103949","journal-title":"IEEE Trans. Image Process."},{"key":"48_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3472080","volume":"62","author":"Y Cui","year":"2024","unstructured":"Cui, Y., et al.: Mapping land-cover dynamics in arid regions using spectral mixture analysis and representative training samples. IEEE Trans. Geosci. Remote Sens. 62, 1\u201313 (2024). https:\/\/doi.org\/10.1109\/TGRS.2024.3472080","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"48_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2025.3548697","author":"J Gao","year":"2025","unstructured":"Gao, J., Shi, J., Zhu, F.: Robust sparse unmixing via continuous mixed norm to address mixed noise. IEEE Geosci. Remote Sens. Lett. (2025). https:\/\/doi.org\/10.1109\/LGRS.2025.3548697","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"48_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3393570","volume":"62","author":"B Rasti","year":"2024","unstructured":"Rasti, B., Zouaoui, A., Mairal, J., Chanussot, J.: Image processing and machine learning for hyperspectral unmixing: an overview and the HySUPP Python package. IEEE Trans. Geosci. Remote Sens. 62, 1\u201331 (2024). https:\/\/doi.org\/10.1109\/TGRS.2024.3393570","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"48_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3405528","volume":"62","author":"W Leng","year":"2024","unstructured":"Leng, W., Han, X., Deng, J., Zhang, H., Li, W., Sun, W.: Spectral super-resolution by using universal and private jointed spectral library and its applications. IEEE Trans. Geosci. Remote Sens. 62, 1\u201311 (2024). https:\/\/doi.org\/10.1109\/TGRS.2024.3405528","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"48_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3249344","volume":"61","author":"Y Ma","year":"2023","unstructured":"Ma, Y., Cai, S., Zhou, J.: Adaptive reference-related graph embedding for hyperspectral anomaly detection. IEEE Trans. Geosci. Remote Sens. 61, 1\u201314 (2023). https:\/\/doi.org\/10.1109\/TGRS.2023.3249344","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"48_CR24","unstructured":"Grupo de Inteligencia Computacional (GIC), Pavia University: Hyperspectral Remote Sensing Scenes. https:\/\/www.ehu.eus\/ccwintco\/index.php?title=Hyperspectral_Remote_Sensing_Scenes#Pavia_University_scene. Accessed May 2025"},{"key":"48_CR25","unstructured":"ECOSTRESS Spectral Library. https:\/\/speclib.jpl.nasa.gov. Accessed May 2025"}],"container-title":["Lecture Notes in Computer Science","Advances in Neural Networks \u2013 ISNN 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-1233-1_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T14:50:28Z","timestamp":1775141428000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-1233-1_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,19]]},"ISBN":["9789819512324","9789819512331"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-1233-1_48","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,19]]},"assertion":[{"value":"19 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISNN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhangye","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isnn2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conference.cs.cityu.edu.hk\/isnn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}