{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:54:24Z","timestamp":1776182064904,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T00:00:00Z","timestamp":1530748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Laboratory of Green Platemaking and Standardization for Flexography Printing","award":["Grant No. ZBKT201710"],"award-info":[{"award-number":["Grant No. ZBKT201710"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recent research shows that deep-learning-derived methods based on a deep convolutional neural network have high accuracy when applied to hyperspectral image (HSI) classification, but long training times. To reduce the training time and improve accuracy, in this paper we propose an end-to-end fast dense spectral\u2013spatial convolution (FDSSC) framework for HSI classification. The FDSSC framework uses different convolutional kernel sizes to extract spectral and spatial features separately, and the \u201cvalid\u201d convolution method to reduce the high dimensions. Densely-connected structures\u2014the input of each convolution consisting of the output of all previous convolution layers\u2014was used for deep learning of features, leading to extremely accurate classification. To increase speed and prevent overfitting, the FDSSC framework uses a dynamic learning rate, parametric rectified linear units, batch normalization, and dropout layers. These attributes enable the FDSSC framework to achieve accuracy within as few as 80 epochs. The experimental results show that with the Indian Pines, Kennedy Space Center, and University of Pavia datasets, the proposed FDSSC framework achieved state-of-the-art performance compared with existing deep-learning-based methods while significantly reducing the training time.<\/jats:p>","DOI":"10.3390\/rs10071068","type":"journal-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T10:52:44Z","timestamp":1530787964000},"page":"1068","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":346,"title":["A Fast Dense Spectral\u2013Spatial Convolution Network Framework for Hyperspectral Images Classification"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8549-4710","authenticated-orcid":false,"given":"Wenju","family":"Wang","sequence":"first","affiliation":[{"name":"College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai SH 021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3231-8817","authenticated-orcid":false,"given":"Shuguang","family":"Dou","sequence":"additional","affiliation":[{"name":"College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai SH 021, China"}]},{"given":"Zhongmin","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai SH 021, China"}]},{"given":"Liujie","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai SH 021, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/JSTARS.2010.2095495","article-title":"High Performance Computing for Hyperspectral Remote Sensing","volume":"4","author":"Plaza","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/S0034-4257(00)00111-5","article-title":"A Hyperspectral Method for Remotely Sensing the Grain Size of Snow","volume":"74","author":"Nolin","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mohanty, P.C., Panditrao, S., Mahendra, R.S., Kumar, S., and Kumar, T.S. (2016, January 4\u20137). Identification of Coral Reef Feature Using Hyperspectral Remote Sensing. Proceedings of the SPIE-The International Society for Optical Engineering, New Delhi, India.","DOI":"10.1117\/12.2227991"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of Hyperspectral Remote Sensing Images with Support Vector Machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TGRS.2014.2381602","article-title":"Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.ins.2014.12.025","article-title":"A Hyperspectral Image Classification Framework and Its Application","volume":"299","author":"Deng","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep Learning-Based Classification of Hyperspectral Data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A., and Doulamis, N. (2015, January 26\u201331). Deep Supervised Learning for Hyperspectral Data Classification through Convolutional Neural Networks. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","article-title":"Spectral-Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., and Shen, Q. (2017). Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network. Remote Sens., 9.","DOI":"10.3390\/rs9010067"},{"key":"ref_13","first-page":"1","article-title":"Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework","volume":"99","author":"Zhong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/LGRS.2016.2630045","article-title":"Deep Learning with Grouped Features for Spatial Spectral Classification of Hyperspectral Images","volume":"14","author":"Zhou","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.isprsjprs.2016.09.001","article-title":"Semisupervised Classification for Hyperspectral Image Based on Multi-Decision Labeling and Deep Feature Learning","volume":"120","author":"Ma","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.11.042620","article-title":"Deep Convolutional Neural Networks for Building Extraction from Orthoimages and Dense Image Matching Point Clouds","volume":"11","author":"Maltezos","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Weinberger, K.Q. (2017, January 25\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Pattern Recognition and Computer Vision (CVPR), College Park, MD, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 11\u201318). Delving Deep into Rectifiers: Surpassing Human-Level Performance on Imagenet Classification. Proceedings of the 15th IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_21","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet Classification with Deep Convolutional Neural Networks. Proceedings of the 26th Annual Conference on Neural Information Processing Systems 2012, Lake Tahoe, NV, USA."},{"key":"ref_22","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lile, France."},{"key":"ref_23","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_24","first-page":"26","article-title":"Lecture 6.5-Rmsprop: Divide the Gradient by a Running Average of Its Recent Magnitude","volume":"4","author":"Tijmen","year":"2012","journal-title":"COURSERA Neural Netw. Mach. Learn."},{"key":"ref_25","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations 2015, San Diego, CA, USA."},{"key":"ref_26","unstructured":"Sutskever, I., Martens, J., Dahl, G., and Hinton, G. (2013, January 16\u201321). On the Importance of Initialization and Momentum in Deep Learning. Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA."},{"key":"ref_27","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the Difficulty of Training Deep Feedforward Neural Networks. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010, Sardinia, Italy."},{"key":"ref_28","unstructured":"Al-Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., and Belopolsky, A. (2016). Theano: A Python Framework for Fast Computation of Mathematical Expressions. arXiv."},{"key":"ref_29","unstructured":"Fran\u00e7ois, C., and Keras-Team (2018, June 01). Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_30","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (arXiv, 2016). Tensorflow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/1068\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:11:24Z","timestamp":1760195484000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/1068"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,5]]},"references-count":30,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2018,7]]}},"alternative-id":["rs10071068"],"URL":"https:\/\/doi.org\/10.3390\/rs10071068","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,5]]}}}