{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T20:19:26Z","timestamp":1783023566833,"version":"3.54.6"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819219254","type":"print"},{"value":"9789819219261","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-1926-1_25","type":"book-chapter","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T15:45:23Z","timestamp":1782747923000},"page":"309-321","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Physically Motivated Partial-Spectrum Denoising for\u00a0Robust Hyperspectral Anomaly Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7528-0676","authenticated-orcid":false,"given":"Jungkwon","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0392-0865","authenticated-orcid":false,"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0428-0417","authenticated-orcid":false,"given":"Jihun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4491-1824","authenticated-orcid":false,"given":"Jeonghyeon","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0366-567X","authenticated-orcid":false,"given":"Kwangsun","family":"Yoo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6110-2543","authenticated-orcid":false,"given":"Seok-Joo","family":"Byun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey. arXiv preprint (2019)","DOI":"10.1145\/3394486.3406704"},{"issue":"6","key":"25_CR2","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1109\/TGRS.2002.800280","volume":"40","author":"C-I Chang","year":"2002","unstructured":"Chang, C.-I., Chiang, S.-S.: Anomaly detection and classification for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 40(6), 1314\u20131325 (2002)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Chang, S.G., Yu, B. Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9(9), 1532\u20131546 (2000)","DOI":"10.1109\/83.862633"},{"issue":"3","key":"25_CR4","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1109\/18.382009","volume":"41","author":"DL Donoho","year":"1995","unstructured":"Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41(3), 613\u2013627 (1995)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"3","key":"25_CR5","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1093\/biomet\/81.3.425","volume":"81","author":"DL Donoho","year":"1994","unstructured":"Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3), 425\u2013455 (1994)","journal-title":"Biometrika"},{"issue":"4","key":"25_CR6","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1109\/MGRS.2017.2762087","volume":"5","author":"P Ghamisi","year":"2017","unstructured":"Ghamisi, P., et al.: Advances in hyperspectral image and signal processing: a comprehensive overview of the state of the art. IEEE Geosci. Remote Sens. Mag. 5(4), 37\u201378 (2017)","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"25_CR7","doi-asserted-by":"publisher","first-page":"5021","DOI":"10.1109\/TSP.2021.3106450","volume":"69","author":"A John","year":"2021","unstructured":"John, A., Sadasivan, J., Seelamantula, C.S.: Adaptive Savitzky-Golay filtering in non-gaussian noise. IEEE Trans. Sig. Process. 69, 5021\u20135036 (2021)","journal-title":"IEEE Trans. Sig. Process."},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Landgrebe, D. A., Malaret, E.: Noise in remote-sensing systems: the effect on classification error. IEEE Trans. Geosci. Remote Sens. GE-24(2), 294\u2013300 (1986)","DOI":"10.1109\/TGRS.1986.289648"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, M., Yoon, J., Yoo, K., Byun, S.-J.: Anomaly detection with hyperspectral imaging for food safety inspection. IEEE Dataport (2024)","DOI":"10.1109\/ACCESS.2024.3505147"},{"key":"25_CR10","doi-asserted-by":"publisher","first-page":"175535","DOI":"10.1109\/ACCESS.2024.3505147","volume":"12","author":"J Lee","year":"2024","unstructured":"Lee, J., Kim, M., Yoon, J., Yoo, K., Byun, S.-J.: PA2E: real-time anomaly detection with hyperspectral imaging for food safety inspection. IEEE Access 12, 175535\u2013175549 (2024)","journal-title":"IEEE Access"},{"key":"25_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107007","volume":"198","author":"Y Liu","year":"2022","unstructured":"Liu, Y., et al.: Joint optimization of autoencoder and self-supervised classifier: anomaly detection of strawberries using hyperspectral imaging. Comput. Electron. Agric. 198, 107007 (2022)","journal-title":"Comput. Electron. Agric."},{"issue":"8","key":"25_CR12","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13311","volume":"40","author":"H Mangotra","year":"2023","unstructured":"Mangotra, H., Srivastava, S., Jaiswal, G., Rani, R., Sharma, A.: Hyperspectral imaging for early diagnosis of diseases: a review. Expert. Syst. 40(8), e13311 (2023)","journal-title":"Expert. Syst."},{"key":"25_CR13","doi-asserted-by":"crossref","unstructured":"Pang, G., Shen, C., Cao, L., van den Hengel, A.: Deep learning for anomaly detection: a review. ACM Comput. Surv. 54(38) (2021)","DOI":"10.1145\/3439950"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Pang, G., Shen, C., van den Hengel, A.: Deep anomaly detection with deviation networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), pp. 353\u2013362 (2019)","DOI":"10.1145\/3292500.3330871"},{"issue":"8","key":"25_CR15","doi-asserted-by":"publisher","first-page":"4391","DOI":"10.1109\/TGRS.2018.2818159","volume":"56","author":"Y Qu","year":"2018","unstructured":"Qu, Y., et al.: Hyperspectral anomaly detection through spectral unmixing and dictionary-based low-rank decomposition. IEEE Trans. Geosci. Remote Sens. 56(8), 4391\u20134405 (2018)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Ruffin, C., King, R. L.: The analysis of hyperspectral data using Savitzky-Golay filtering-theoretical basis. In: IEEE International Geoscience and Remote Sensing Symposium, vol. 2, pp. 756\u2013758 (1999)","DOI":"10.1109\/IGARSS.1999.774430"},{"issue":"8","key":"25_CR17","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1021\/ac60214a047","volume":"36","author":"A Savitzky","year":"1964","unstructured":"Savitzky, A., Golay, M.J.E.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627\u20131639 (1964)","journal-title":"Anal. Chem."},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, T., Fu Y., Li, C.: Hyperspectral image denoising with realistic data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 2248\u20132257 (2021)","DOI":"10.1109\/ICCV48922.2021.00225"}],"container-title":["Lecture Notes in Computer Science","Data Science: Foundations and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-1926-1_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:41:00Z","timestamp":1783021260000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-1926-1_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"ISBN":["9789819219254","9789819219261"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-1926-1_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,30]]},"assertion":[{"value":"30 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","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":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pakdd2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}