{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T05:02:46Z","timestamp":1764997366022,"version":"3.41.2"},"reference-count":42,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T00:00:00Z","timestamp":1620864000000},"content-version":"vor","delay-in-days":132,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The existing work on unsupervised segmentation frequently does not present any statistical extent to estimating and equating procedures, gratifying a qualitative calculation. Furthermore, regardless of the datum that enormous research is dedicated to the advancement of a novel segmentation approach and upgrading the deep learning techniques, there is an absence of research comprehending the assessment of eminent conventional segmentation methodologies for HSI. In this paper, to moderately fill this gap, we propose a direct method that diminishes the issues to some extent with the deep learning methods in the arena of a HSI space and evaluate the proposed segmentation techniques based on the method of the clustering\u2010based profound iterating deep learning model for HSI segmentation termed as CPIDM. The proposed model is an unsupervised HSI clustering technique centered on the density of pixels in the spectral interplanetary space and the distance concerning the pixels. Furthermore, CPIDM is a fully convolutional neural network. In general, fully convolutional nets remain spatially invariant preventing them from modeling position\u2010reliant outlines. The proposed network maneuvers this by encompassing an innovative position inclined convolutional stratum. The anticipated unique edifice of deep unsupervised segmentation deciphers the delinquency of oversegmentation and nonlinearity of data due to noise and outliers. The spectrum efficacy is erudite and incidental from united feedback via deep hierarchy with pooling and convolutional strata; as a consequence, it formulates an affiliation among class dissemination and spectra along with three\u2010dimensional features. Moreover, the anticipated deep learning model has revealed that it is conceivable to expressively accelerate the segmentation process without substantive quality loss due to the existence of noise and outliers. The proposed CPIDM approach outperforms many state\u2010of\u2010the\u2010art segmentation approaches that include watershed transform and neuro\u2010fuzzy approach as validated by the experimental consequences.<\/jats:p>","DOI":"10.1155\/2021\/7279260","type":"journal-article","created":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T22:35:12Z","timestamp":1620945312000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["CPIDM: A Clustering\u2010Based Profound Iterating Deep Learning Model for HSI Segmentation"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4791-6000","authenticated-orcid":false,"given":"Kriti","family":"Mahajan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1846-1353","authenticated-orcid":false,"given":"Urvashi","family":"Garg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5106-7609","authenticated-orcid":false,"given":"Mohammad","family":"Shabaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,5,13]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2005.846154"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2014.2329330"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2011.2129595"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2013.2286953"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.272"},{"key":"e_1_2_9_8_2","doi-asserted-by":"crossref","unstructured":"CagnazzoM. PoggiG. andVerdolivaL. A comparison of flat and object-based transform coding techniques for the compression of multispectral images IEEE International Conference on Image Processing 2005 2005 Genova Italy 657\u2013660 https:\/\/doi.org\/10.1109\/icip.2005.1529836 2-s2.0-33749633977.","DOI":"10.1109\/ICIP.2005.1529836"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/36.752192"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/36.843010"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2003.10.018"},{"key":"e_1_2_9_12_2","doi-asserted-by":"crossref","unstructured":"SilvermannJ. RotmanS. R. andCaeferC. E. Segmentation of hyperspectral images from the histograms of principle components 4816 Imaging Spectrometry VIII Nov. 2002 270\u2013277.","DOI":"10.1117\/12.451537"},{"key":"e_1_2_9_13_2","unstructured":"MuhammedH. H. Unsupervised hyperspectral image segmentation using a new class of neuro-fuzzy systems based on weighted incremental neural networks Applied Imagery Pattern Recognition Workshop 2002. Proceedings Oct. 2002 Washington DC USA 171\u2013177."},{"key":"e_1_2_9_14_2","doi-asserted-by":"crossref","unstructured":"SharmaA. AnsariM. D. andKumarR. A comparative study of edge detectors in digital image processing 2017 4th International Conference on Signal Processing Computing and Control (ISPCC) 2017 Solan India 246\u2013250.","DOI":"10.1109\/ISPCC.2017.8269683"},{"key":"e_1_2_9_15_2","doi-asserted-by":"crossref","unstructured":"MercierG. DerrodeS. andLennonM. Hyperspectral image segmentation with Markov chain model 6 IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477) Jul. 2003 Toulouse France 3766\u20133768.","DOI":"10.1109\/IGARSS.2003.1295263"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2003.1295256"},{"key":"e_1_2_9_17_2","unstructured":"HongP. S. KaplanL. M. andSmithM. J. T. Hyperspectral image segmentation using filter banks for texture augmentation IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data 2003 2003 Greenbelt MD USA 254\u2013258."},{"key":"e_1_2_9_18_2","doi-asserted-by":"crossref","unstructured":"MohammadpourA. F\u00e9ronO. andMohammad-DjafariA. Bayesian segmentation of hyperspectral images AIP Conference Proceedings Jul. 2004 Maxent Workshop 541\u2013548.","DOI":"10.1063\/1.1835254"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5580098"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/0031-3203(81)90028-5"},{"key":"e_1_2_9_21_2","doi-asserted-by":"crossref","unstructured":"TarabalkaY. ChanussotJ. BenediktssonJ. A. AnguloJ. andFauvelM. Segmentation and classification of hyperspectral data using watershed IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium 2008 Boston MA III-652\u2013III-655.","DOI":"10.1109\/IGARSS.2008.4779432"},{"volume-title":"Use of Watersheds in Contour Detection","year":"1979","author":"Beucher S.","key":"e_1_2_9_22_2"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056489"},{"key":"e_1_2_9_24_2","unstructured":"ArthurD.andVassilvitskiiS. K-means++: the advantages of careful seeding SODA\u203207 Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms 2007 2007 1027\u20131035."},{"key":"e_1_2_9_25_2","doi-asserted-by":"crossref","unstructured":"Meil\u0103M.andSch\u00f6lkopfB. WarmuthM. K. Comparing clusterings by the variation of information Learning Theory and Kernel MachinesLNCS 2003 https:\/\/doi.org\/10.1007\/978-3-540-45167-9_14.","DOI":"10.1007\/978-3-540-45167-9_14"},{"volume-title":"Digital Image Processing","year":"2002","author":"Gonzalez R.","key":"e_1_2_9_26_2"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.2174\/2213275912666181207152633"},{"volume-title":"Morphological Image Analysis","year":"2003","author":"Soille P.","key":"e_1_2_9_28_2"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.5566\/ias.v26.p101-109"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2008.2002952"},{"key":"e_1_2_9_31_2","doi-asserted-by":"crossref","unstructured":"MakantasisK. KarantzalosK. DoulamisA. andDoulamisN. Deep supervised learning for hyperspectral data classification through convolutional neural networks 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Jul 2015 Milan Italy 4959\u20134962 https:\/\/doi.org\/10.1109\/igarss.2015.7326945 2-s2.0-84962569483.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2015.2388577"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"volume-title":"Spectral\u2013Spatial Classification of Hyperspectral Remote Sensing Images","year":"2015","author":"Benediktsson J. A.","key":"e_1_2_9_34_2"},{"key":"e_1_2_9_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2014.2337320"},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2006.880628"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2014.2367010"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2013.2279179"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2004.831865"},{"key":"e_1_2_9_40_2","article-title":"Schroedinger eigenmaps with nondiagonal potentials for spatial-spectral clustering of hyperspectral imagery","volume":"9088","author":"Cahill N. D.","year":"2014","journal-title":"Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX"},{"key":"e_1_2_9_41_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.4756257"},{"key":"e_1_2_9_42_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5536170"}],"container-title":["Wireless Communications and Mobile Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/7279260.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/7279260.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/7279260","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T10:51:40Z","timestamp":1723027900000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/7279260"}},"subtitle":[],"editor":[{"given":"Vimal","family":"Shanmuganathan","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/7279260"],"URL":"https:\/\/doi.org\/10.1155\/2021\/7279260","archive":["Portico"],"relation":{},"ISSN":["1530-8669","1530-8677"],"issn-type":[{"type":"print","value":"1530-8669"},{"type":"electronic","value":"1530-8677"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-04-06","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-30","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"7279260"}}