{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T03:11:49Z","timestamp":1775099509393,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s12145-021-00731-1","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:03:47Z","timestamp":1641859427000},"page":"383-395","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Hyperspectral image classification based on optimized convolutional neural networks with 3D stacked blocks"],"prefix":"10.1007","volume":"15","author":[{"given":"Xiaoxia","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yong","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Xia","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"731_CR1","unstructured":"Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. arXiv preprint arXiv:1607.06450"},{"issue":"6","key":"731_CR2","doi-asserted-by":"publisher","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","volume":"7","author":"YS Chen","year":"2014","unstructured":"Chen YS, Lin ZH, Zhao X et al (2014) Deep learning-based classification of Hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 7(6):2094\u20132107","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing"},{"issue":"6","key":"731_CR3","doi-asserted-by":"publisher","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","volume":"8","author":"YS Chen","year":"2015","unstructured":"Chen YS, Zhao X, Jia XP (2015) Spectral\u2013spatial classification of hyperspectral data based on deep belief network. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 8(6):2381\u20132392","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing"},{"issue":"10","key":"731_CR4","doi-asserted-by":"publisher","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","volume":"54","author":"YS Chen","year":"2016","unstructured":"Chen YS, Jiang HL, Li CY et al (2016) Deep feature extraction and classification of Hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232\u20136251","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"11","key":"731_CR5","doi-asserted-by":"publisher","first-page":"6712","DOI":"10.1109\/TGRS.2018.2841823","volume":"56","author":"G Cheng","year":"2018","unstructured":"Cheng G, Li ZP, Han JW et al (2018) Exploring hierarchical convolutional features for hyperspectral image classification. IEEE Trans Geosci Remote Sens 56(11):6712\u20136722","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"11","key":"731_CR6","doi-asserted-by":"publisher","first-page":"3804","DOI":"10.1109\/TGRS.2008.922034","volume":"46","author":"M Fauvel","year":"2008","unstructured":"Fauvel M, Benediktsson JA, Chanussot J et al (2008) Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Trans Geosci Remote Sens 46(11):3804\u20133814","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"4","key":"731_CR7","doi-asserted-by":"publisher","first-page":"2442","DOI":"10.1109\/TGRS.2016.2645226","volume":"55","author":"J Geng","year":"2017","unstructured":"Geng J, Wang HY, Fan JC et al (2017) Deep supervised and contractive neural network for SAR image classification. IEEE Trans Geosci Remote Sens 55(4):2442\u20132459","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"731_CR8","first-page":"315","volume":"15","author":"X Glorot","year":"2011","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. J Mach Learn Res 15:315\u2013323","journal-title":"J Mach Learn Res"},{"issue":"8","key":"731_CR9","doi-asserted-by":"publisher","first-page":"4420","DOI":"10.1109\/TGRS.2018.2818945","volume":"56","author":"AB Hamida","year":"2018","unstructured":"Hamida AB, Benoit A, Lambert P et al (2018) 3-D deep learning approach for remote sensing image classification. IEEE Trans Geosci Remote Sens 56(8):4420\u20134434","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"731_CR10","doi-asserted-by":"crossref","unstructured":"He M, Li B, Chen H (2018) Multi-scale 3D deep convolutional neural network for hyperspectral image classification. In: 2017 IEEE International Conference on Image Processing (ICIP)","DOI":"10.1109\/ICIP.2017.8297014"},{"key":"731_CR11","doi-asserted-by":"crossref","unstructured":"Huang X, Serge B (2017) Arbitrary style transfer in real-time with adaptive instance normalization. In: 2017 IEEE International Conference on Computer Vision (ICCV)","DOI":"10.1109\/ICCV.2017.167"},{"issue":"1","key":"731_CR12","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1109\/TGRS.2014.2319373","volume":"53","author":"XD Kang","year":"2014","unstructured":"Kang XD, Li ST, Fang LY et al (2014) Extended random Walker-based classification of Hyperspectral images. IEEE Trans Geosci Remote Sens 53(1):144\u2013153","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"12","key":"731_CR13","doi-asserted-by":"publisher","first-page":"7140","DOI":"10.1109\/TGRS.2017.2743102","volume":"55","author":"XD Kang","year":"2017","unstructured":"Kang XD, Xiang XL, Li ST et al (2017) PCA-based edge-preserving features for Hyperspectral image classification. IEEE Trans Geosci Remote Sens 55(12):7140\u20137151","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"731_CR14","doi-asserted-by":"publisher","first-page":"14118","DOI":"10.1109\/ACCESS.2018.2812999","volume":"6","author":"MJ Khan","year":"2018","unstructured":"Khan MJ, Khan HS, Yousaf A et al (2018) Modern trends in Hyperspectral image analysis: a review. IEEE Access 6:14118\u201314129","journal-title":"IEEE Access"},{"issue":"1","key":"731_CR15","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/JSTARS.2014.2362769","volume":"8","author":"H Khurshid","year":"2015","unstructured":"Khurshid H, Khan MF (2015) Segmentation and classification using logistic regression in remote sensing imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(1):224\u2013232","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"731_CR16","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. International Conference on Learning Representations"},{"issue":"10","key":"731_CR17","doi-asserted-by":"publisher","first-page":"4843","DOI":"10.1109\/TIP.2017.2725580","volume":"26","author":"H Lee","year":"2017","unstructured":"Lee H, Kwon H (2017) Going deeper with contextual CNN for hyperspectral image classification. IEEE Trans Image Process 26(10):4843\u20134855","journal-title":"IEEE Trans Image Process"},{"key":"731_CR18","doi-asserted-by":"crossref","unstructured":"Leng J, Li T, Bai G et al (2017) Cube-CNN-SVM: a novel Hyperspectral image classification method. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1027\u20131034","DOI":"10.1109\/ICTAI.2016.0158"},{"key":"731_CR19","doi-asserted-by":"crossref","unstructured":"L\u00e9on B (2010) Large-scale machine learning with stochastic gradient descent. Proc of COMPSTAT:177\u2013186","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"731_CR20","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.compag.2012.01.010","volume":"83","author":"XH Li","year":"2012","unstructured":"Li XH, Lee WS, Li MZ et al (2012) Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Computers & Electronics in Agriculture 83:32\u201346","journal-title":"Computers & Electronics in Agriculture"},{"issue":"1","key":"731_CR21","doi-asserted-by":"publisher","first-page":"67","DOI":"10.3390\/rs9010067","volume":"9","author":"Y Li","year":"2017","unstructured":"Li Y, Zhang H, Shen Q (2017) Spectral-spatial classification of Hyperspectral imagery with 3D convolutional neural network. Remote Sens 9(1):67","journal-title":"Remote Sens"},{"key":"731_CR22","doi-asserted-by":"crossref","unstructured":"Li CM, Wang YC, Zhang XK et al (2019) Deep Belief Network for Spectral\u2013Spatial Classification of Hyperspectral Remote Sensor Data. Sensors 19(1)","DOI":"10.3390\/s19010204"},{"key":"731_CR23","unstructured":"Loffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167"},{"key":"731_CR24","doi-asserted-by":"crossref","unstructured":"Makantasis K, Karantzalos K, Doulamis A et al (2015) Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pp. 4959\u20134962","DOI":"10.1109\/IGARSS.2015.7326945"},{"issue":"2","key":"731_CR25","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1137\/100802001","volume":"22","author":"Y Nesterov","year":"2012","unstructured":"Nesterov Y (2012) Efficiency of coordinate descent methods on huge-scale optimization problems. SIAM J Optim 22(2):341\u2013362","journal-title":"SIAM J Optim"},{"issue":"12","key":"731_CR26","doi-asserted-by":"publisher","first-page":"2403","DOI":"10.1109\/LGRS.2015.2478966","volume":"12","author":"K Qi","year":"2015","unstructured":"Qi K, Wu H, Shen C et al (2015) Land-use scene classification in high-resolution remote sensing images using improved Correlatons. IEEE Geosci Remote Sens Lett 12(12):2403\u20132407","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"731_CR27","unstructured":"Rodarmel C, Shan J (2017) Principal component analysis for Hyperspectral image classification. Engineering of Surveying & Mapping"},{"issue":"2","key":"731_CR28","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1109\/JSTARS.2013.2237757","volume":"6","author":"S Samiappan","year":"2013","unstructured":"Samiappan S, Prasad S, Bruce LM (2013) Non-uniform random feature selection and kernel density scoring with SVM based ensemble classification for Hyperspectral image analysis. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 6(2):792\u2013800","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing"},{"issue":"6","key":"731_CR29","doi-asserted-by":"publisher","first-page":"3173","DOI":"10.1109\/TGRS.2018.2794326","volume":"56","author":"WW Song","year":"2018","unstructured":"Song WW, Li ST, Fang LY et al (2018) Hyperspectral image classification with deep feature fusion network. IEEE Trans Geosci Remote Sens 56(6):3173\u20133184","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"731_CR30","first-page":"2818","volume-title":"Rethinking the inception architecture for computer vision","author":"C Szegedy","year":"2016","unstructured":"Szegedy C, Vanhoucke V, Ioffe S et al (2016) Rethinking the inception architecture for computer vision. Computer vision and pattern recognition, In, pp 2818\u20132826"},{"key":"731_CR31","doi-asserted-by":"crossref","unstructured":"Wei W, Zhang JY, Zhang L et al (2018) Deep cube-pair network for Hyperspectral imagery classification. Remote Sens 10(5)","DOI":"10.3390\/rs10050783"},{"key":"731_CR32","doi-asserted-by":"crossref","unstructured":"Wu Y, He KM (2018) Group normalization. Int J Comput Vis 742\u2013755","DOI":"10.1007\/s11263-019-01198-w"},{"key":"731_CR33","doi-asserted-by":"crossref","unstructured":"Yang LX, Yang SY, Jin PL e al (2014) Semi-supervised Hyperspectral image classification using Spatio-spectral Laplacian support vector machine. IEEE Geoscience & Remote Sensing Letters 11(3):651\u2013655","DOI":"10.1109\/LGRS.2013.2273792"},{"key":"731_CR34","doi-asserted-by":"crossref","unstructured":"Yan L, Zhu RX, Liu Y et al (2018) Color-boosted saliency-guided rotation invariant bag of visual words representation with parameter transfer for cross-domain scene-level classification. Remote Sens 10(4)","DOI":"10.3390\/rs10040610"},{"issue":"8","key":"731_CR35","doi-asserted-by":"publisher","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","volume":"54","author":"WZ Zhao","year":"2016","unstructured":"Zhao WZ, Du SH (2016) Spectral\u2013spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach. IEEE Trans Geosci Remote Sens 54(8):4544\u20134554","journal-title":"IEEE Trans Geosci Remote Sens"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-021-00731-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-021-00731-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-021-00731-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T02:15:46Z","timestamp":1644459346000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-021-00731-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,11]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["731"],"URL":"https:\/\/doi.org\/10.1007\/s12145-021-00731-1","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,11]]},"assertion":[{"value":"23 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}