{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:56:48Z","timestamp":1760597808156,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,3,8]],"date-time":"2018-03-08T00:00:00Z","timestamp":1520467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Project of the National Natural Science Foundation of China","award":["61231016"],"award-info":[{"award-number":["61231016"]}]},{"name":"China 863 Program","award":["2015AA016402","2015AA016402"],"award-info":[{"award-number":["2015AA016402","2015AA016402"]}]},{"name":"National Natural Science Foundations of China","award":["61471297","61671385","61301192","61771397"],"award-info":[{"award-number":["61471297","61671385","61301192","61771397"]}]},{"name":"Shaanxi International Cooperation Project","award":["2017KW-006"],"award-info":[{"award-number":["2017KW-006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Considering kernels in Convolutional Neural Networks (CNNs) as detectors for local patterns, K-means neural network proposes to cluster local patches extracted from training images and then fixate those kernels as the representative patches in each cluster without further training. Thus the amount of labeled samples necessitated for training can be greatly reduced. One key property of those kernels is their spatial size which determines their capacity in detecting local patterns and is expected to be task-specific. However, most of literatures determine the spatial size of those kernels in a heuristic way. To address this problem, we propose to automatically determine the kernel size in order to better adapt the K-means neural network for hyperspectral imagery classification. Specifically, a novel kernel-size determination scheme is developed by measuring the clustering performance of local patches with different sizes. With the kernel of determined size, more discriminative local patterns can be detected in the hyperspectral imagery, with which the classification performance of K-means neural network can be obviously improved. Experimental results on two datasets demonstrate the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/rs10030415","type":"journal-article","created":{"date-parts":[[2018,3,8]],"date-time":"2018-03-08T12:07:33Z","timestamp":1520510853000},"page":"415","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Automatic Kernel Size Determination for Deep Neural Networks Based Hyperspectral Image Classification"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8101-5738","authenticated-orcid":false,"given":"Chen","family":"Ding","sequence":"first","affiliation":[{"name":"Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Yong","family":"Xia","sequence":"additional","affiliation":[{"name":"Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Yanning","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/79.974718","article-title":"Hyperspectral image data analysis","volume":"19","author":"Landgrebe","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Richards, J.A., and Richards, J. (1999). Remote Sensing Digital Image Analysis, Springer.","DOI":"10.1007\/978-3-662-03978-6"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zortea, M., De Martino, M., and Serpico, S. (2007, January 23\u201328). A SVM Ensemble Approach for Spectral-Contextual Classification of Optical High Spatial Resolution Imagery. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4423090"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4173","DOI":"10.1109\/TGRS.2008.2002577","article-title":"An adaptive mean-shift analysis approach for object extraction and classification from urban hyperspectral imagery","volume":"46","author":"Huang","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TGRS.2011.2162649","article-title":"Spectral\u2013spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields","volume":"50","author":"Li","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1080\/2150704X.2015.1029087","article-title":"Latent subclass learning-based unsupervised ensemble feature extraction method for hyperspectral image classification","volume":"6","author":"Wei","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4974","DOI":"10.1109\/TIP.2016.2598652","article-title":"Exploring Structured Sparsity by a Reweighted Laplace Prior for Hyperspectral Compressive Sensing","volume":"25","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7223","DOI":"10.1109\/TGRS.2016.2598577","article-title":"Dictionary learning for promoting structured sparsity in hyperspectral compressive sensing","volume":"54","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","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_11","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_12","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_13","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_14","unstructured":"Sch\u00f6lkopf, B., Platt, J., and Hofmann, T. (2006, January 3\u20136). Greedy Layer-Wise Training of Deep Networks. Proceedings of the International Conference on Neural Information Processing Systems, Hong Kong, China."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2381","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_16","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_17","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. Comput. Sci."},{"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 Computer Vision and Pattern Recognition, 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. (2015, January 7\u201312). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wei, W., Zhang, Y., Shen, C., Aton, V., and Qin, S. (2018). Cluster Sparsity Field: An Internal Hyperspectral Imagery Prior for Reconstruction. Int. J. Comput. Vis., accepted.","DOI":"10.1007\/s11263-018-1080-8"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhang, L., Wei, W., and Zhang, Y. (2018). When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature. Remote Sens., 10.","DOI":"10.3390\/rs10020284"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2016.09.010","article-title":"Convolutional neural networks for hyperspectral image classification","volume":"219","author":"Yu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"5017","DOI":"10.1109\/TIP.2015.2475625","article-title":"PCANet: A Simple Deep Learning Baseline for Image Classification?","volume":"24","author":"Chan","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, D., Ying, L., Yong, X., Wei, W., Lei, Z., and Zhang, Y. (2017). Convolutional Neural Networks Based Hyperspectral Image Classification Method with Adaptive Kernels. Remote Sens., 9.","DOI":"10.3390\/rs9060618"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Coates, A., and Ng, A.Y. (2012). Learning Feature Representations with K-Means, Springer.","DOI":"10.1007\/978-3-642-35289-8_30"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"L\u00e4ngkvist, M., Kiselev, A., Alirezaie, M., and Loutfi, A. (2016). Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks. Remote Sens., 8.","DOI":"10.3390\/rs8040329"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, S., Xing, J., Niu, Z., Shan, S., and Yan, S. (2015, January 7\u201312). Shape Driven Kernel Adaptation in Convolutional Neural Network for Robust Facial Trait Recognition. Proceedings of the IEEE Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298618"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Postavaru, S., Stoean, R., Stoean, C., and Caparros, G.J. (2017, January 14). Adaptation of Deep Convolutional Neural Networks for Cancer Grading from Histopathological Images. Proceedings of the International Work-Conference on Artificial Neural Networks, Cadiz, Spain.","DOI":"10.1007\/978-3-319-59147-6_4"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TETC.2014.2330519","article-title":"A survey of clustering algorithms for big data: Taxonomy and empirical analysis","volume":"2","author":"Fahad","year":"2014","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1145\/568574.568575","article-title":"Why so many clustering algorithms: A position paper","volume":"4","year":"2002","journal-title":"ACM SIGKDD Exp. Newslett."},{"key":"ref_32","unstructured":"F\u00e4rber, I., G\u00fcnnemann, S., Kriegel, H.-P., Kr\u00f6ger, P., M\u00fcller, E., Schubert, E., Seidl, T., and Zimek, A. (2010, January 25\u201328). On Using Class-Labels in Evaluation of Clusterings. Proceedings of the MultiClust: 1st International Workshop on Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with KDD, Washington, DC, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s40745-015-0040-1","article-title":"A comprehensive survey of clustering algorithms","volume":"2","author":"Xu","year":"2015","journal-title":"Ann. Data Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/3\/415\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:58:03Z","timestamp":1760194683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/3\/415"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,8]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2018,3]]}},"alternative-id":["rs10030415"],"URL":"https:\/\/doi.org\/10.3390\/rs10030415","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,3,8]]}}}