{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T15:23:04Z","timestamp":1793114584788,"version":"4.5.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,6,22]],"date-time":"2020-06-22T00:00:00Z","timestamp":1592784000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,22]],"date-time":"2020-06-22T00:00:00Z","timestamp":1592784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Hong Kong Research Grants Council","award":["Project C1007-15G"],"award-info":[{"award-number":["Project C1007-15G"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s00521-020-05071-7","type":"journal-article","created":{"date-parts":[[2020,6,22]],"date-time":"2020-06-22T09:02:41Z","timestamp":1592816561000},"page":"2047-2063","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Multi-deep features fusion for high-resolution remote sensing image scene classification"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9694-9250","authenticated-orcid":false,"given":"Baohua","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixin","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangping","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,6,22]]},"reference":[{"key":"5071_CR1","unstructured":"Andrew G, Arora R, Bilmes J, Livescu K (2013) Deep canonical correlation analysis. In: International conference on international conference on machine learning, pp III\u20131247"},{"key":"5071_CR2","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.isprsjprs.2018.01.023","volume":"138","author":"RM Anwer","year":"2018","unstructured":"Anwer RM, Khan FS, van de Weijer J, Molinier M, Laaksonen J (2018) Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification. ISPRS J Photogramm Remote Sens 138:74\u201385","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"5071_CR3","unstructured":"Castelluccio M, Poggi G, Sansone C, Verdoliva L (2015) Land use classification in remote sensing images by convolutional neural networks. arXiv preprint arXiv:1508.00092"},{"issue":"8","key":"5071_CR4","doi-asserted-by":"crossref","first-page":"4775","DOI":"10.1109\/TGRS.2017.2700322","volume":"55","author":"S Chaib","year":"2017","unstructured":"Chaib S, Liu H, Gu Y, Yao H (2017) Deep feature fusion for VHR remote sensing scene classification. IEEE Trans Geosci Remote Sens 55(8):4775\u20134784","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"3","key":"5071_CR5","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27. https:\/\/doi.org\/10.1145\/1961189.1961199","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"issue":"2","key":"5071_CR6","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1109\/TGRS.2017.2760909","volume":"56","author":"B Chaudhuri","year":"2018","unstructured":"Chaudhuri B, Demir B, Chaudhuri S, Bruzzone L (2018) Multilabel remote sensing image retrieval using a semisupervised graph-theoretic method. IEEE Trans Geosci Remote Sens 56(2):1144\u20131158","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"4","key":"5071_CR7","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1007\/s11760-015-0804-2","volume":"10","author":"C Chen","year":"2016","unstructured":"Chen C, Zhang B, Su H, Li W, Wang L (2016) Land-use scene classification using multi-scale completed local binary patterns. Signal Image video Process 10(4):745\u2013752. https:\/\/doi.org\/10.1007\/s11760-015-0804-2","journal-title":"Signal Image video Process"},{"issue":"4","key":"5071_CR8","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1109\/TGRS.2014.2351395","volume":"53","author":"S Chen","year":"2015","unstructured":"Chen S, Tian Y (2015) Pyramid of spatial relatons for scene-level land use classification. IEEE Trans Geosci Remote Sens 53(4):1947\u20131957. https:\/\/doi.org\/10.1109\/TGRS.2014.2351395","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"10","key":"5071_CR9","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","volume":"105","author":"G Cheng","year":"2017","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","journal-title":"Proc IEEE"},{"issue":"5","key":"5071_CR10","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1109\/TGRS.2017.2783902","volume":"56","author":"G Cheng","year":"2018","unstructured":"Cheng G, Yang C, Yao X, Guo L, Han J (2018) When deep learning meets metric learning: remote sensing image scene classification via learning discriminative cnns. IEEE Trans Geosci Remote Sens 56(5):2811\u20132821","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"12","key":"5071_CR11","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":"5071_CR12","first-page":"008","volume":"11","author":"PYZ Dao-Qiang","year":"2008","unstructured":"Dao-Qiang PYZ (2008) Semi-supervised canonical correlation analysis algorithm. J Softw 11:008","journal-title":"J Softw"},{"issue":"1","key":"5071_CR13","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.cviu.2005.09.012","volume":"106","author":"L Fei-Fei","year":"2007","unstructured":"Fei-Fei L, Fergus R, Perona P (2007) Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. Comput Vis Image Underst 106(1):59\u201370","journal-title":"Comput Vis Image Underst"},{"key":"5071_CR14","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.patcog.2018.12.019","volume":"89","author":"E Flores","year":"2019","unstructured":"Flores E, Zortea M, Scharcanski J (2019) Dictionaries of deep features for land-use scene classification of very high spatial resolution images. Pattern Recognit 89:32\u201344","journal-title":"Pattern Recognit"},{"key":"5071_CR15","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/LGRS.2018.2803259","volume":"15","author":"S Georganos","year":"2018","unstructured":"Georganos S, Grippa T, Vanhuysse S, Lennert M, Shimoni M, Wolff E (2018) Very high resolution object-based land use-land cover urban classification using extreme gradient boosting. IEEE Geosci Remote Sens Lett 15:607\u2013611","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"5071_CR16","doi-asserted-by":"crossref","first-page":"67200","DOI":"10.1109\/ACCESS.2019.2918732","volume":"7","author":"Y Guo","year":"2019","unstructured":"Guo Y, Ji J, Lu X, Huo H, Fang T, Li D (2019) Global-local attention network for aerial scene classification. IEEE Access 7:67200\u201367212","journal-title":"IEEE Access"},{"issue":"9","key":"5071_CR17","doi-asserted-by":"crossref","first-page":"1984","DOI":"10.1109\/TIFS.2016.2569061","volume":"11","author":"M Haghighat","year":"2016","unstructured":"Haghighat M, Abdel-Mottaleb M, Alhalabi W (2016) Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition. IEEE Trans Inf Forensics Secur 11(9):1984\u20131996","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"5071_CR18","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.isprsjprs.2017.11.004","volume":"145","author":"W Han","year":"2018","unstructured":"Han W, Feng R, Wang L, Cheng Y (2018) A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification. ISPRS J Photogramm Remote Sens 145:23\u201343","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"5071_CR19","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3\/4","key":"5071_CR20","doi-asserted-by":"crossref","first-page":"321","DOI":"10.2307\/2333955","volume":"28","author":"H Hotelling","year":"1936","unstructured":"Hotelling H (1936) Relations between two sets of variates. Biometrika 28(3\/4):321\u2013377","journal-title":"Biometrika"},{"issue":"11","key":"5071_CR21","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","volume":"7","author":"F Hu","year":"2015","unstructured":"Hu F, Xia GS, Hu J, Zhang L (2015) Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens 7(11):14680\u201314707","journal-title":"Remote Sens"},{"key":"5071_CR22","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger K.Q (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"issue":"14","key":"5071_CR23","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.3390\/rs11141687","volume":"11","author":"H Huang","year":"2019","unstructured":"Huang H, Xu K (2019) Combing triple-part features of convolutional neural networks for scene classification in remote sensing. Remote Sens 11(14):1687","journal-title":"Remote Sens"},{"issue":"1","key":"5071_CR24","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1080\/10106049.2016.1222637","volume":"33","author":"T Kassawmar","year":"2018","unstructured":"Kassawmar T, Eckert S, Hurni K, Zeleke G, Hurni H (2018) Reducing landscape heterogeneity for improved land use and land cover (lulc) classification across the large and complex ethiopian highlands. Geocarto Int 33(1):53\u201369","journal-title":"Geocarto Int"},{"key":"5071_CR25","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"key":"5071_CR26","doi-asserted-by":"publisher","unstructured":"Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201906), vol 2, pp 2169\u20132178. IEEE. https:\/\/doi.org\/10.1109\/CVPR.2006.68","DOI":"10.1109\/CVPR.2006.68"},{"key":"5071_CR27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patcog.2017.06.036","volume":"73","author":"K Li","year":"2018","unstructured":"Li K, Zou C, Bu S, Liang Y, Zhang J, Gong M (2018) Multi-modal feature fusion for geographic image annotation. Pattern Recognit 73:1\u201314","journal-title":"Pattern Recognit"},{"issue":"1","key":"5071_CR28","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s11063-018-9825-5","volume":"49","author":"L Li","year":"2019","unstructured":"Li L, Ge H, Gao J, Zhang Y (2019) Hyperspectral image feature extraction using Maclaurin series function curve fitting. Neural Process Lett 49(1):357\u2013374","journal-title":"Neural Process Lett"},{"issue":"1","key":"5071_CR29","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s11063-019-10101-0","volume":"51","author":"L Li","year":"2020","unstructured":"Li L, Ge H, Gao J, Zhang Y, Tong Y, Sun J (2020) A novel geometric mean feature space discriminant analysis method for hyperspectral image feature extraction. Neural Process Lett 51(1):515\u2013542","journal-title":"Neural Process Lett"},{"issue":"2","key":"5071_CR30","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1109\/TGRS.2017.2756911","volume":"56","author":"Y Li","year":"2018","unstructured":"Li Y, Zhang Y, Huang X, Zhu H, Ma J (2018) Large-scale remote sensing image retrieval by deep hashing neural networks. IEEE Trans Geosci Remote Sens 56(2):950\u2013965","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"8","key":"5071_CR31","doi-asserted-by":"crossref","first-page":"2911","DOI":"10.1109\/JSTARS.2018.2836671","volume":"11","author":"M Liang","year":"2018","unstructured":"Liang M, Jiao L, Yang S, Liu F, Hou B, Chen H (2018) Deep multiscale spectral-spatial feature fusion for hyperspectral images classification. IEEE J Sel Top Appl Earth Obs Remote Sens 11(8):2911\u20132924","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"issue":"8","key":"5071_CR32","doi-asserted-by":"publisher","first-page":"2554","DOI":"10.1016\/j.patcog.2015.02.005","volume":"48","author":"G Liu","year":"2015","unstructured":"Liu G, Yang J, Li Z (2015) Content-based image retrieval using computational visual attention model. Pattern Recognit 48(8):2554\u20132566. https:\/\/doi.org\/10.1016\/j.patcog.2015.02.005","journal-title":"Pattern Recognit"},{"issue":"2","key":"5071_CR33","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1109\/LGRS.2017.2779469","volume":"15","author":"Y Liu","year":"2018","unstructured":"Liu Y, Liu Y, Ding L (2018) Scene classification based on two-stage deep feature fusion. IEEE Geosci Remote Sens Lett 15(2):183\u2013186","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"1","key":"5071_CR34","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1109\/LGRS.2015.2499239","volume":"13","author":"D Marmanis","year":"2016","unstructured":"Marmanis D, Datcu M, Esch T, Stilla U (2016) Deep learning earth observation classification using imagenet pretrained networks. IEEE Geosci Remote Sens Lett 13(1):105\u2013109. https:\/\/doi.org\/10.1109\/LGRS.2015.2499239","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"9","key":"5071_CR35","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1016\/S0031-3203(03)00058-X","volume":"36","author":"T Melzer","year":"2003","unstructured":"Melzer T, Reiter M, Bischof H (2003) Appearance models based on kernel canonical correlation analysis. Pattern Recognit 36(9):1961\u20131971","journal-title":"Pattern Recognit"},{"key":"5071_CR36","doi-asserted-by":"crossref","unstructured":"Miao Q, Li Y, Ouyang W, Ma Z, Xu X, Shi W, Cao X (2018) Multimodal gesture recognition based on the resc3d network. In: IEEE International conference on computer vision workshop, pp 3047\u20133055","DOI":"10.1109\/ICCVW.2017.360"},{"issue":"4","key":"5071_CR37","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1109\/TSMCB.2008.2009071","volume":"39","author":"MM Monwar","year":"2009","unstructured":"Monwar MM, Gavrilova ML (2009) Multimodal biometric system using rank-level fusion approach. IEEE Trans Syst Man Cybern Part B (Cybern) 39(4):867\u2013878","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybern)"},{"issue":"5","key":"5071_CR38","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1080\/01431161.2017.1399472","volume":"39","author":"P Napoletano","year":"2018","unstructured":"Napoletano P (2018) Visual descriptors for content-based retrieval of remote-sensing images. Int J Remote Sens 39(5):1343\u20131376","journal-title":"Int J Remote Sens"},{"key":"5071_CR39","doi-asserted-by":"publisher","unstructured":"Nogueira K, Penatti O, dos Santos J (2016, 2017) Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognit 61:539\u2013556. https:\/\/doi.org\/10.1016\/j.patcog.2016.07.001","DOI":"10.1016\/j.patcog.2016.07.001"},{"issue":"5","key":"5071_CR40","doi-asserted-by":"crossref","first-page":"165","DOI":"10.3390\/ijgi7050165","volume":"7","author":"A Nurwanda","year":"2018","unstructured":"Nurwanda A, Honjo T (2018) Analysis of land use change and expansion of surface urban heat island in bogor city by remote sensing. ISPRS Int J Geo-Inf 7(5):165","journal-title":"ISPRS Int J Geo-Inf"},{"issue":"10","key":"5071_CR41","doi-asserted-by":"crossref","first-page":"2149","DOI":"10.1080\/01431161.2016.1171928","volume":"37","author":"E Othman","year":"2016","unstructured":"Othman E, Bazi Y, Alajlan N, Alhichri H, Melgani F (2016) Using convolutional features and a sparse autoencoder for land-use scene classification. Int J Remote Sens 37(10):2149\u20132167","journal-title":"Int J Remote Sens"},{"issue":"2","key":"5071_CR42","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1007\/s11769-018-0946-6","volume":"28","author":"ISS Pathiranage","year":"2018","unstructured":"Pathiranage ISS, Kantakumar LN, Sundaramoorthy S (2018) Remote sensing data and sleuth urban growth model: as decision support tools for urban planning. Chin Geogr Sci 28(2):274\u2013286","journal-title":"Chin Geogr Sci"},{"key":"5071_CR43","doi-asserted-by":"crossref","unstructured":"Penatti OA, Nogueira K, dos Santos JA (2015) Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 44\u201351","DOI":"10.1109\/CVPRW.2015.7301382"},{"key":"5071_CR44","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"issue":"6","key":"5071_CR45","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.1109\/TGRS.2018.2794326","volume":"56","author":"W Song","year":"2018","unstructured":"Song W, Li S, Fang L, Lu T (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"},{"issue":"3","key":"5071_CR46","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.patcog.2004.08.009","volume":"38","author":"QS Sun","year":"2005","unstructured":"Sun QS, Liu ZD, Heng PA, Xia DS (2005) A theorem on the generalized canonical projective vectors. Pattern Recognit 38(3):449\u2013452","journal-title":"Pattern Recognit"},{"issue":"12","key":"5071_CR47","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1016\/j.patcog.2004.12.013","volume":"38","author":"QS Sun","year":"2005","unstructured":"Sun QS, Zeng SG, Liu Y, Heng PA, Xia DS (2005) A new method of feature fusion and its application in image recognition. Pattern Recognit 38(12):2437\u20132448","journal-title":"Pattern Recognit"},{"key":"5071_CR48","doi-asserted-by":"crossref","unstructured":"Sun T, Chen S, Yang J, Shi P (2008) A novel method of combined feature extraction for recognition. In: Eighth IEEE international conference on data mining, 2008. ICDM\u201908. IEEE, pp 1043\u20131048","DOI":"10.1109\/ICDM.2008.28"},{"key":"5071_CR49","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI, vol 4, p 12","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"5071_CR50","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"5071_CR51","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"issue":"2","key":"5071_CR52","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/TGRS.2018.2864987","volume":"57","author":"Q Wang","year":"2018","unstructured":"Wang Q, Liu S, Chanussot J, Li X (2018) Scene classification with recurrent attention of vhr remote sensing images. IEEE Trans Geosci Remote Sens 57(2):1155\u20131167","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5071_CR53","unstructured":"Wang W, Arora R, Livescu K, Bilmes J (2015) On deep multi-view representation learning. In: International conference on machine learning, pp 1083\u20131092"},{"issue":"7","key":"5071_CR54","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","volume":"55","author":"GS Xia","year":"2017","unstructured":"Xia GS, Hu J, Hu F, Shi B, Bai X, Zhong Y, Zhang L, Lu X (2017) Aid: a benchmark data set for performance evaluation of aerial scene classification. IEEE Trans Geosci Remote Sens 55(7):3965\u20133981","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5071_CR55","first-page":"1","volume":"99","author":"K Xu","year":"2020","unstructured":"Xu K, Huang H, Li Y, Shi G (2020) Multilayer feature fusion network for scene classification in remote sensing. IEEE Geosci Remote Sens Lett 99:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"5071_CR56","doi-asserted-by":"crossref","unstructured":"Yang J, Jiang YG, Hauptmann AG, Ngo CW (2007) Evaluating bag-of-visual-words representations in scene classification. In: Proceedings of the international workshop on multimedia information retrieval. ACM, pp 197\u2013206","DOI":"10.1145\/1290082.1290111"},{"issue":"6","key":"5071_CR57","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1016\/S0031-3203(02)00262-5","volume":"36","author":"J Yang","year":"2003","unstructured":"Yang J, Yang JY, Zhang D, Lu JF (2003) Feature fusion: parallel strategy vs. serial strategy. Pattern Recognit 36(6):1369\u20131381","journal-title":"Pattern Recognit"},{"key":"5071_CR58","doi-asserted-by":"publisher","unstructured":"Yang Y, Newsam S (2010) Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 270\u2013279. https:\/\/doi.org\/10.1145\/1869790.1869829","DOI":"10.1145\/1869790.1869829"},{"key":"5071_CR59","doi-asserted-by":"crossref","unstructured":"Yu Y, Gong Z, Wang C, Zhong P, (2017, 2018) An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geosci Remote Sens Lett 15(1):23\u201327","DOI":"10.1109\/LGRS.2017.2767626"},{"issue":"1","key":"5071_CR60","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1109\/TGRS.2019.2937830","volume":"58","author":"Y Yu","year":"2019","unstructured":"Yu Y, Li X, Liu F (2019) Attention gans: unsupervised deep feature learning for aerial scene classification. IEEE Trans Geosci Remote Sens 58(1):519\u2013531","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5071_CR61","first-page":"1","volume":"2018","author":"Y Yu","year":"2018","unstructured":"Yu Y, Liu F (2018) A two-stream deep fusion framework for high-resolution aerial scene classification. Comput Intell Neurosci 2018:1\u201313","journal-title":"Comput Intell Neurosci"},{"issue":"1","key":"5071_CR62","doi-asserted-by":"crossref","first-page":"015010","DOI":"10.1117\/1.JRS.12.015010","volume":"12","author":"B Yuan","year":"2018","unstructured":"Yuan B, Li S, Li N (2018) Multiscale deep features learning for land-use scene recognition. J Appl Remote Sens 12(1):015010","journal-title":"J Appl Remote Sens"},{"issue":"3","key":"5071_CR63","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1109\/TGRS.2015.2488681","volume":"54","author":"F Zhang","year":"2016","unstructured":"Zhang F, Du B, Zhang L (2016) Scene classification via a gradient boosting random convolutional network framework. IEEE Trans Geosci Remote Sens 54(3):1793\u20131802","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"5071_CR64","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","volume":"4","author":"L Zhang","year":"2016","unstructured":"Zhang L, Zhang L, Du B (2016) Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geosci Remote Sens Mag 4(2):22\u201340","journal-title":"IEEE Geosci Remote Sens Mag"},{"key":"5071_CR65","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.isprsjprs.2018.01.004","volume":"145","author":"W Zhou","year":"2018","unstructured":"Zhou W, Newsam S, Li C, Shao Z (2018) Patternnet: a benchmark dataset for performance evaluation of remote sensing image retrieval. ISPRS J Photogramm Remote Sens 145:197\u2013209","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"4","key":"5071_CR66","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1109\/TGRS.2017.2769113","volume":"56","author":"J Zhu","year":"2018","unstructured":"Zhu J, Hu J, Jia S, Jia X, Li Q (2018) Multiple 3-d feature fusion framework for hyperspectral image classification. IEEE Trans Geosci Remote Sens 56(4):1873\u20131886","journal-title":"IEEE Trans Geosci Remote Sens"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05071-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05071-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05071-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T18:58:56Z","timestamp":1667069936000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05071-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,22]]},"references-count":66,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["5071"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05071-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,22]]},"assertion":[{"value":"14 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}