{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:41:18Z","timestamp":1776444078462,"version":"3.51.2"},"reference-count":47,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,13]],"date-time":"2017-01-13T00:00:00Z","timestamp":1484265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2016YFB0502502"],"award-info":[{"award-number":["2016YFB0502502"]}]},{"name":"the Natural Science Basic Research Plan in Shaanxi Province of China","award":["2015JM6296"],"award-info":[{"award-number":["2015JM6296"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recent research has shown that using spectral\u2013spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral\u2013spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral\u2013spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods\u2014namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods\u2014on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record.<\/jats:p>","DOI":"10.3390\/rs9010067","type":"journal-article","created":{"date-parts":[[2017,1,13]],"date-time":"2017-01-13T10:08:37Z","timestamp":1484302117000},"page":"67","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1157,"title":["Spectral\u2013Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network"],"prefix":"10.3390","volume":"9","author":[{"given":"Ying","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, Shaanxi, China"}]},{"given":"Haokui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, Shaanxi, China"}]},{"given":"Qiang","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,13]]},"reference":[{"key":"ref_1","unstructured":"Lacar, F.M., Lewis, M.M., and Grierson, I.T. (2001, January 9\u201313). Use of hyperspectral imagery for mapping grape varieties in the Barossa Valley, South Australia. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Sydney, Australia."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"Geosci. Remote Sens. Mag."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/S0031-3203(99)00215-0","article-title":"A Linear Constrained distance-based discriminant analysis for hyperspectral image classification","volume":"34","author":"Du","year":"2001","journal-title":"Pattern Recognit."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.1109\/TGRS.2008.916629","article-title":"Supervised classification of remotely sensed imagery using a modified, k-NN technique","volume":"46","author":"Samaniego","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1109\/36.602523","article-title":"Hierarchical maximum-likelihood classification for improved accuracies","volume":"35","author":"Ediriwickrema","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","first-page":"4085","article-title":"Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning","volume":"48","author":"Li","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","unstructured":"Donoho, D.L. (August,  6\u201311). High-dimensional data analysis: The curses and blessings of dimensionality. Proceedings of the AMS Math Challenges Lecture, Los Angeles, CA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2012.2197589","article-title":"Adcances in spectral-spatial classification of hyperspectral images","volume":"101","author":"Fauvel","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1016\/j.patcog.2004.01.006","article-title":"A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profiles","volume":"37","author":"Plaza","year":"2004","journal-title":"Pattern Recognit."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TGRS.2004.842478","article-title":"Classification of hyperspectral data from urban areas based on extended morphological profiles","volume":"43","author":"Benediktsson","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1109\/TGRS.2014.2358934","article-title":"A survey on spectral\u2013spatial classification techniques based on attribute profiles","volume":"53","author":"Ghamisi","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6062","DOI":"10.1109\/TGRS.2013.2294724","article-title":"Automatic feature learning for spatio-spectral image classification with sparse SVM","volume":"52","author":"Tuia","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1109\/LGRS.2010.2091253","article-title":"Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis","volume":"8","author":"Villa","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1109\/JSTARS.2015.2423278","article-title":"Spectral\u2013spatial hyperspectral image classification using regularized low-rank representation and sparse representation-based graph cuts","volume":"8","author":"Jia","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, X., Xu, C., Li, M., and Sun, X. (2015). Sparse and Low-rank coupling image segmentation model via nonconvex regularization. Int. J. Pattern Recognit. Artif. Intell., 29.","DOI":"10.1142\/S0218001415550046"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1109\/LGRS.2011.2145353","article-title":"Adaptive Markov Random field approach for classification of hyperspectral imagery","volume":"8","author":"Zhang","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tarabalka, Y., and Rana, A. (2014, January 13\u201318). Graph-cut-based model for spectral-spatial classification of hyperspectral images. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6947216"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3571","DOI":"10.3390\/rs4113571","article-title":"Improving Wishart classification of polarimetric SAR data using the Hopfield neural network optimization approach","volume":"4","author":"Pajares","year":"2012","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7132","DOI":"10.3390\/s90907132","article-title":"Image-based airborne sensors: A combined approach for spectral signatures classification through deterministic simulated annealing","volume":"9","author":"Guijarro","year":"2009","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1016\/j.isprsjprs.2011.09.007","article-title":"Improving the Wishart synthetic aperture radar image classifications through deterministic simulated annealing","volume":"66","author":"Pajares","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1109\/JSTARS.2014.2303634","article-title":"An adaptive Memetic fuzzy clustering algorithm with spatial information for remote sensing imagery","volume":"7","author":"Zhong","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2276","DOI":"10.1109\/TGRS.2012.2209657","article-title":"Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features","volume":"51","author":"Qian","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5039","DOI":"10.1109\/TGRS.2011.2157166","article-title":"Three-dimensional Gabor wavelets for pixel-based hyperspectral imagery classification","volume":"49","author":"Shen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2467","DOI":"10.1109\/TGRS.2014.2360672","article-title":"Hyperspectral image classification based on three-dimensional scattering wavelet transform","volume":"53","author":"Tang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/TGRS.2012.2197860","article-title":"Tensor discriminative locality alignment for hyperspectral image spectral\u2013spatial feature extraction","volume":"53","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3102","DOI":"10.1016\/j.patcog.2014.12.016","article-title":"Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding","volume":"48","author":"Zhang","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_28","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_29","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_30","doi-asserted-by":"crossref","unstructured":"Li, T., Zhang, J., and Zhang, Y. (2014, January 27\u201330). Classification of hyperspectral image based on deep belief networks. Proceedings of the 2014 IEEE International Conference on Image Processing, Paris, France.","DOI":"10.1109\/ICIP.2014.7026039"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spectral\u2013spatial classification of hyperspectral data based on deep belief network","volume":"8","author":"Chen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","article-title":"Spectral\u2013spatial 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_33","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/2150704X.2015.1047045","article-title":"Spectral-spatial classification of hyperspectral images using deep convolutional neural networks","volume":"6","author":"Yue","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_34","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 IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liang, H., and Li, Q. (2016). Hyperspectral imagery classification using sparse representations of convolutional neural network features. Remote Sens., 8.","DOI":"10.3390\/rs8020099"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3D convolutional neural networks for human action recognition","volume":"35","author":"Ji","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015, January 7\u201313). Learning spatiotemporal features with 3D convolutional networks. Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref_38","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., and Sermanet, P. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, F., Shen, C., and Lin, G. (2015, January 7\u201312). Deep convolutional neural fields for depth estimation from a single image. Proceedings of the Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299152"},{"key":"ref_44","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_45","unstructured":"Palm, R.B. Prediction as a Candidate for Learning Deep Hierarchical Models of Data. Available online: https:\/\/github.com\/rasmusbergpalm\/DeepLearnToolbox."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., and Lenc, K. (2015, January 26\u201330). MatConvNet: Convolutional neural networks for MATLAB. Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia.","DOI":"10.1145\/2733373.2807412"},{"key":"ref_47","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/1\/67\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:26:10Z","timestamp":1760207170000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/1\/67"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,1,13]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,1]]}},"alternative-id":["rs9010067"],"URL":"https:\/\/doi.org\/10.3390\/rs9010067","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,1,13]]}}}