{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T13:07:16Z","timestamp":1758892036899,"version":"3.37.3"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2018,1,8]],"date-time":"2018-01-08T00:00:00Z","timestamp":1515369600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61501456"],"award-info":[{"award-number":["61501456"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Light of West China","award":["XAB2016B20"],"award-info":[{"award-number":["XAB2016B20"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1007\/s11042-017-5552-6","type":"journal-article","created":{"date-parts":[[2018,1,8]],"date-time":"2018-01-08T17:19:13Z","timestamp":1515431953000},"page":"29759-29777","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Hyperspectral image classification based on joint spectrum of spatial space and spectral space"],"prefix":"10.1007","volume":"77","author":[{"given":"Xiaorong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhibin","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqiang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingliang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,1,8]]},"reference":[{"issue":"3","key":"5552_CR1","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TGRS.2004.842478","volume":"43","author":"JA Benediktsson","year":"2005","unstructured":"Benediktsson JA, Palmason JA, Sveinsson JR (2005) Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans Geosci Remote Sens 43(3):480\u2013491","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"5552_CR2","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JSTARS.2012.2194696","volume":"5","author":"JM Bioucas-Dias","year":"2012","unstructured":"Bioucas-Dias JM et al (2012) Hyperspectral Unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J Sel Top Appl Earth Observation Remote Sens 5(2):354\u2013379","journal-title":"IEEE J Sel Top Appl Earth Observation Remote Sens"},{"key":"5552_CR3","doi-asserted-by":"crossref","unstructured":"Cahill ND, Czaja W, Messinger DW (2014) Schroedinger Eigenmaps with nondiagonal potentials for spatial-spectral clustering of hyperspectral imagery, in SPIE Defense + Security, vol. 9088, p. 908804: SPIE","DOI":"10.1117\/12.2050651"},{"issue":"1","key":"5552_CR4","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1109\/LGRS.2005.857031","volume":"3","author":"G Camps-Valls","year":"2006","unstructured":"Camps-Valls G, Gomez-Chova L, Munoz-Mari J, Vila-Frances J, Calpe-Maravilla J (2006) Composite kernels for hyperspectral image classification. IEEE Geosci Remote Sens Lett 3(1):93\u201397","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"5552_CR5","doi-asserted-by":"publisher","unstructured":"Cao X, Han J, Yang S, Tao D, Jiao L (2016) Band selection and evaluation with spatial information. Int J Remote Sens 37(19):4501\u20134520. https:\/\/doi.org\/10.1080\/01431161.2016.1214301","DOI":"10.1080\/01431161.2016.1214301"},{"key":"5552_CR6","unstructured":"Chang CI (2006) Hyperspectral Data Exploitation: Theory and Applications. pp 47\u201374"},{"issue":"6","key":"5552_CR7","doi-asserted-by":"crossref","first-page":"5795","DOI":"10.3390\/rs6065795","volume":"6","author":"C Chen","year":"2014","unstructured":"Chen C, Li W, Su H, Liu K (2014) Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine. Remote Sens 6(6):5795","journal-title":"Remote Sens"},{"issue":"12","key":"5552_CR8","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","volume":"54","author":"G Cheng","year":"2016","unstructured":"Cheng G, Zhou P, Han J (2016) Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans Geosci Remote Sens 54(12):7405\u20137415","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5552_CR9","doi-asserted-by":"publisher","unstructured":"Cheng G, Han J, Lu X (2017) Remote sensing image scene classification: benchmark and state of the art. Proc IEEE 105(10):1865\u20131883. https:\/\/doi.org\/10.1109\/JPROC.2017.2675998","DOI":"10.1109\/JPROC.2017.2675998"},{"issue":"22","key":"5552_CR10","doi-asserted-by":"crossref","first-page":"5975","DOI":"10.1080\/01431161.2010.512425","volume":"31","author":"M Dalla Mura","year":"2010","unstructured":"Dalla Mura M, Atli Benediktsson J, Waske B, Bruzzone L (2010) Extended profiles with morphological attribute filters for the analysis of hyperspectral data. Int J Remote Sens 31(22):5975\u20135991","journal-title":"Int J Remote Sens"},{"key":"5552_CR11","doi-asserted-by":"crossref","unstructured":"Demirci S, Erer I, Ersoy O (2015) Weighted Chebyshev distance classification method for hyperspectral imaging, in SPIE Sensing Technology + Applications, vol. {9482}, p. 948218: SPIE","DOI":"10.1117\/12.2181914"},{"issue":"11","key":"5552_CR12","doi-asserted-by":"publisher","first-page":"5427","DOI":"10.1109\/TIP.2016.2607421","volume":"25","author":"G Ding","year":"2016","unstructured":"Ding G, Guo Y, Zhou J, Gao Y (2016) Large-scale cross-modality search via collective matrix factorization hashing. IEEE Trans Image Process 25(11):5427\u20135440. https:\/\/doi.org\/10.1109\/TIP.2016.2607421","journal-title":"IEEE Trans Image Process"},{"issue":"11","key":"5552_CR13","doi-asserted-by":"crossref","first-page":"6844","DOI":"10.1109\/TGRS.2014.2303895","volume":"52","author":"B Du","year":"2014","unstructured":"Du B, Zhang L (2014) A discriminative metric learning based anomaly detection method. IEEE Trans Geosci Remote Sens 52(11):6844\u20136857","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"1","key":"5552_CR14","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.patcog.2013.07.005","volume":"47","author":"B Du","year":"2014","unstructured":"Du B, Zhang L (2014) Target detection based on a dynamic subspace. Pattern Recogn 47(1):344\u2013358","journal-title":"Pattern Recogn"},{"issue":"3","key":"5552_CR15","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1016\/j.patcog.2012.09.009","volume":"46","author":"M Fauvel","year":"2013","unstructured":"Fauvel M, Chanussot J, Benediktsson JA, Villa A (2013) Parsimonious Mahalanobis kernel for the classification of high dimensional data. Pattern Recogn 46(3):845\u2013854","journal-title":"Pattern Recogn"},{"issue":"6","key":"5552_CR16","doi-asserted-by":"crossref","first-page":"2824","DOI":"10.1109\/JSTARS.2015.2441771","volume":"8","author":"M Fauvel","year":"2015","unstructured":"Fauvel M, Dechesne C, Zullo A, Ferraty F (2015) Fast forward feature selection of hyperspectral images for classification with Gaussian mixture models. IEEE J Sel Top Appl Earth Observation Remote Sens 8(6):2824\u20132831","journal-title":"IEEE J Sel Top Appl Earth Observation Remote Sens"},{"key":"5552_CR17","doi-asserted-by":"crossref","unstructured":"Gharaati E, Nasri M (2015) A new band selection method for hyperspectral images based on constrained optimization. pp 1\u20136","DOI":"10.1109\/IKT.2015.7288779"},{"key":"5552_CR18","unstructured":"Ghedass F, Farah IR (2015) An improved classification of hyperspectral imaging based on spectral signature and gray level co-occurrence matrix. Available: http:\/\/ceur-ws.org\/Vol-1535\/paper-19.pdf"},{"issue":"3","key":"5552_CR19","doi-asserted-by":"publisher","first-page":"1344","DOI":"10.1109\/TIP.2017.2652730","volume":"26","author":"Y Guo","year":"2017","unstructured":"Guo Y, Ding G, Liu L, Han J, Shao L (2017) Learning to hash with optimized anchor embedding for scalable retrieval. IEEE Trans Image Process 26(3):1344\u20131354. https:\/\/doi.org\/10.1109\/TIP.2017.2652730","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"5552_CR20","doi-asserted-by":"crossref","first-page":"3325","DOI":"10.1109\/TGRS.2014.2374218","volume":"53","author":"J Han","year":"2015","unstructured":"Han J, Zhang D, Cheng G, Guo L, Ren J (2015) Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Trans Geosci Remote Sens 53(6):3325\u20133337","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"5","key":"5552_CR21","doi-asserted-by":"crossref","first-page":"2666","DOI":"10.1109\/TGRS.2013.2264508","volume":"52","author":"X Kang","year":"2014","unstructured":"Kang X, Li S, Benediktsson JA (2014) Spectral-spatial hyperspectral image classification with edge-preserving filtering. IEEE Trans Geosci Remote Sens 52(5):2666\u20132677","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"10","key":"5552_CR22","doi-asserted-by":"crossref","first-page":"6298","DOI":"10.1109\/TGRS.2013.2296031","volume":"52","author":"M Khodadadzadeh","year":"2014","unstructured":"Khodadadzadeh M, Li J, Plaza A, Ghassemian H, Bioucas-Dias JM, Li X (2014) Spectral-spatial classification of hyperspectral data using local and global probabilities for mixed pixel characterization. IEEE Trans Geosci Remote Sens 52(10):6298\u20136314","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"Supplement C","key":"5552_CR23","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.isprsjprs.2016.05.009","volume":"119","author":"A Kianisarkaleh","year":"2016","unstructured":"Kianisarkaleh A, Ghassemian H (2016) Nonparametric feature extraction for classification of hyperspectral images with limited training samples. ISPRS J Photogramm Remote Sens 119(Supplement C):64\u201378","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"5552_CR24","unstructured":"Landgrebe D (2017) AVIRIS NW Indiana's Indian Pines 1992 data set"},{"issue":"9","key":"5552_CR25","doi-asserted-by":"crossref","first-page":"4816","DOI":"10.1109\/TGRS.2012.2230268","volume":"51","author":"J Li","year":"2013","unstructured":"Li J, Marpu PR, Plaza A, Bioucas-Dias JM, Benediktsson JA (2013) Generalized composite kernel framework for hyperspectral image classification. IEEE Trans Geosci Remote Sens 51(9):4816\u20134829","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"6","key":"5552_CR26","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1109\/JSTARS.2015.2414816","volume":"8","author":"F Li","year":"2015","unstructured":"Li F, Xu L, Siva P, Wong A, Clausi DA (2015) Hyperspectral image classification with limited labeled training samples using enhanced ensemble learning and conditional random fields. IEEE J Sel Top Appl Earth Observation Remote Sens 8(6):2427\u20132438","journal-title":"IEEE J Sel Top Appl Earth Observation Remote Sens"},{"issue":"2","key":"5552_CR27","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/JSTARS.2015.2470129","volume":"9","author":"J Li","year":"2016","unstructured":"Li J, Khodadadzadeh M, Plaza A, Jia X, Bioucas-Dias JM (2016) A discontinuity preserving relaxation scheme for spectral-spatial hyperspectral image classification. IEEE J Sel Top Appl Earth Observation Remote Sens 9(2):625\u2013639","journal-title":"IEEE J Sel Top Appl Earth Observation Remote Sens"},{"issue":"99","key":"5552_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TCYB.2016.2608906","volume":"PP","author":"Z Lin","year":"2017","unstructured":"Lin Z, Ding G, Han J, Wang J (2017) Cross-view retrieval via probability-based semantics-preserving hashing. IEEE Trans Cybern PP(99):1\u201314. https:\/\/doi.org\/10.1109\/TCYB.2016.2608906","journal-title":"IEEE Trans Cybern"},{"issue":"6","key":"5552_CR29","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1109\/TNNLS.2013.2245914","volume":"24","author":"X Lu","year":"2013","unstructured":"Lu X, Wang Y, Yuan Y (2013) Sparse coding from a Bayesian perspective. IEEE Trans Neural Netw Learn Syst 24(6):929\u2013939. https:\/\/doi.org\/10.1109\/TNNLS.2013.2245914","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"7","key":"5552_CR30","doi-asserted-by":"publisher","first-page":"4009","DOI":"10.1109\/TGRS.2012.2226730","volume":"51","author":"X Lu","year":"2013","unstructured":"Lu X, Wang Y, Yuan Y (2013) Graph-regularized low-rank representation for Destriping of hyperspectral images. IEEE Trans Geosci Remote Sens 51(7):4009\u20134018. https:\/\/doi.org\/10.1109\/TGRS.2012.2226730","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"9","key":"5552_CR31","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.1109\/TCYB.2014.2362959","volume":"45","author":"X Lu","year":"2015","unstructured":"Lu X, Li X, Mou L (2015) Semi-supervised multitask learning for scene recognition. IEEE Trans Cybern 45(9):1967\u20131976","journal-title":"IEEE Trans Cybern"},{"issue":"9","key":"5552_CR32","doi-asserted-by":"crossref","first-page":"5148","DOI":"10.1109\/TGRS.2017.2702596","volume":"55","author":"X Lu","year":"2017","unstructured":"Lu X, Zheng X, Yuan Y (2017) Remote sensing scene classification by unsupervised representation learning. IEEE Trans Geosci Remote Sens 55(9):5148\u20135157","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5552_CR33","unstructured":"Prasad S, Cui M, Yan L (2016) Composite Kernel Local Angular Discriminant Analysis for Multi-Sensor Geospatial Image Analysis. CoRR, vol. abs\/1607.04939%6, p. %&"},{"issue":"Supplement C","key":"5552_CR34","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.spasta.2016.02.003","volume":"16","author":"A Sellami","year":"2016","unstructured":"Sellami A, Farah IR (2016) High-level hyperspectral image classification based on spectro-spatial dimensionality reduction. Spatial Statistics 16(Supplement C):103\u2013117","journal-title":"Spatial Statistics"},{"issue":"1","key":"5552_CR35","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1109\/TGRS.2014.2325067","volume":"53","author":"A Soltani-Farani","year":"2015","unstructured":"Soltani-Farani A, Rabiee HR, Hosseini SA (2015) Spatial-aware dictionary learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 53(1):527\u2013541","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"6","key":"5552_CR36","doi-asserted-by":"crossref","first-page":"2697","DOI":"10.1109\/JSTARS.2014.2320299","volume":"7","author":"K Sun","year":"2014","unstructured":"Sun K, Geng X, Ji L, Lu Y (2014) A new band selection method for hyperspectral image based on data quality. IEEE J Sel Top Appl Earth Observation Remote Sens 7(6):2697\u20132703","journal-title":"IEEE J Sel Top Appl Earth Observation Remote Sens"},{"issue":"5","key":"5552_CR37","doi-asserted-by":"crossref","first-page":"2532","DOI":"10.1109\/TGRS.2014.2361618","volume":"53","author":"J Xia","year":"2015","unstructured":"Xia J, Chanussot J, Du P, He X (2015) Spectral-spatial classification for hyperspectral data using rotation forests with local feature extraction and Markov random fields. IEEE Trans Geosci Remote Sens 53(5):2532\u20132546","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"Supplement C","key":"5552_CR38","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.neucom.2015.02.073","volume":"164","author":"X Yao","year":"2015","unstructured":"Yao X, Han J, Guo L, Bu S, Liu Z (2015) A coarse-to-fine model for airport detection from remote sensing images using target-oriented visual saliency and CRF. Neurocomputing 164(Supplement C):162\u2013172","journal-title":"Neurocomputing"},{"issue":"6","key":"5552_CR39","doi-asserted-by":"crossref","first-page":"3660","DOI":"10.1109\/TGRS.2016.2523563","volume":"54","author":"X Yao","year":"2016","unstructured":"Yao X, Han J, Cheng G, Qian X, Guo L (2016) Semantic annotation of high-resolution satellite images via weakly supervised learning. IEEE Trans Geosci Remote Sens 54(6):3660\u20133671","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"7","key":"5552_CR40","doi-asserted-by":"crossref","first-page":"3196","DOI":"10.1109\/TIP.2017.2694222","volume":"26","author":"X Yao","year":"2017","unstructured":"Yao X, Han J, Zhang D, Nie F (2017) Revisiting co-saliency detection: a novel approach based on two-stage multi-view spectral rotation co-clustering. IEEE Trans Image Process 26(7):3196\u20133209","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"5552_CR41","doi-asserted-by":"crossref","first-page":"355","DOI":"10.3390\/rs8040355","volume":"8","author":"H Yu","year":"2016","unstructured":"Yu H, Gao L, Li J, Li S, Zhang B, Benediktsson J (2016) Spectral-spatial hyperspectral image classification using subspace-based support vector machines and adaptive Markov random fields. Remote Sens 8(4):355","journal-title":"Remote Sens"},{"issue":"11","key":"5552_CR42","doi-asserted-by":"crossref","first-page":"3488","DOI":"10.1109\/TIP.2015.2446948","volume":"24","author":"J Yuan","year":"2015","unstructured":"Yuan J, Wang D, Cheriyadat AM (2015) Factorization-based texture segmentation. IEEE Trans Image Process 24(11):3488\u20133497","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"5552_CR43","doi-asserted-by":"crossref","first-page":"3516","DOI":"10.1109\/TGRS.2017.2675902","volume":"55","author":"P Zhong","year":"2017","unstructured":"Zhong P, Gong Z, Li S, Sch\u00f6nlieb CB (2017) Learning to diversify deep belief networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55(6):3516\u20133530","journal-title":"IEEE Trans Geosci Remote Sens"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-017-5552-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-017-5552-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-017-5552-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T00:10:37Z","timestamp":1570579837000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-017-5552-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,8]]},"references-count":43,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2018,11]]}},"alternative-id":["5552"],"URL":"https:\/\/doi.org\/10.1007\/s11042-017-5552-6","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2018,1,8]]}}}