{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:21:14Z","timestamp":1769520074294,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,26]],"date-time":"2021-08-26T00:00:00Z","timestamp":1629936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071379, 61901365 and 61571361"],"award-info":[{"award-number":["62071379, 61901365 and 61571361"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2021JM-461 and 2020JM-299"],"award-info":[{"award-number":["2021JM-461 and 2020JM-299"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["GK202103085"],"award-info":[{"award-number":["GK202103085"]}]},{"name":"New Star Team of Xi\u2019an University of Posts &amp; Telecommunications","award":["xyt2016-01"],"award-info":[{"award-number":["xyt2016-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, with the extensive application of deep learning techniques in the hyperspectral image (HSI) field, particularly convolutional neural network (CNN), the research of HSI classification has stepped into a new stage. To avoid the problem that the receptive field of naive convolution is small, the dilated convolution is introduced into the field of HSI classification. However, the dilated convolution usually generates blind spots in the receptive field, resulting in discontinuous spatial information obtained. In order to solve the above problem, a densely connected pyramidal dilated convolutional network (PDCNet) is proposed in this paper. Firstly, a pyramidal dilated convolutional (PDC) layer integrates different numbers of sub-dilated convolutional layers is proposed, where the dilated factor of the sub-dilated convolution increases exponentially, achieving multi-sacle receptive fields. Secondly, the number of sub-dilated convolutional layers increases in a pyramidal pattern with the depth of the network, thereby capturing more comprehensive hyperspectral information in the receptive field. Furthermore, a feature fusion mechanism combining pixel-by-pixel addition and channel stacking is adopted to extract more abstract spectral\u2013spatial features. Finally, in order to reuse the features of the previous layers more effectively, dense connections are applied in densely pyramidal dilated convolutional (DPDC) blocks. Experiments on three well-known HSI datasets indicate that PDCNet proposed in this paper has good classification performance compared with other popular models.<\/jats:p>","DOI":"10.3390\/rs13173396","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T21:59:45Z","timestamp":1630447185000},"page":"3396","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0323-9573","authenticated-orcid":false,"given":"Feng","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Communications and Information Engineering (School of Artificial Intelligence), Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0895-3127","authenticated-orcid":false,"given":"Junjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Communications and Information Engineering (School of Artificial Intelligence), Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2364-2749","authenticated-orcid":false,"given":"Zhe","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Communications and Information Engineering (School of Artificial Intelligence), Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8774-8625","authenticated-orcid":false,"given":"Hanqiang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shaanxi Normal University, Xi\u2019an 710119, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,26]]},"reference":[{"key":"ref_1","unstructured":"Shabbir, S., and Ahmad, M. (2021). Hyperspectral image classification\u2013traditional to deep models: A survey for future prospects. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Han, Y., Li, J., Zhang, Y., Hong, Z., and Wang, J. (2017). Sea ice detection based on an improved similarity measurement method using hyperspectral data. Sensors, 17.","DOI":"10.3390\/s17051124"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Stuart, M.B., McGonigle, A.J., and Willmott, J.R. (2019). Hyperspectral imaging in environmental monitoring: A review of recent developments and technological advances in compact field deployable systems. Sensors, 19.","DOI":"10.3390\/s19143071"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Garzon-Lopez, C.X., and Lasso, E. (2020). Species classification in a tropical alpine ecosystem using UAV-Borne RGB and hyperspectral imagery. Drones, 4.","DOI":"10.3390\/drones4040069"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Borana, S., Yadav, S., and Parihar, S. (2019, January 18\u201319). Hyperspectral data analysis for arid vegetation species: Smart & sustainable growth. Proceedings of the 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India.","DOI":"10.1109\/ICCCIS48478.2019.8974502"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TGRS.2020.2994057","article-title":"Residual spectral\u2013spatial attention network for hyperspectral image classification","volume":"59","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Meng, Z., Jiao, L., Liang, M., and Zhao, F. (2021). A lightweight spectral-spatial convolution module for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2021.3069202"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Signoroni, A., Savardi, M., Baronio, A., and Benini, S. (2019). Deep learning meets hyperspectral image analysis: A multidisciplinary review. J. Imaging, 5.","DOI":"10.3390\/jimaging5050052"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"107298","DOI":"10.1016\/j.patcog.2020.107298","article-title":"Deep support vector machine for hyperspectral image classification","volume":"103","author":"Okwuashi","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sabat-Tomala, A., Raczko, E., and Zagajewski, B. (2020). Comparison of support vector machine and random forest algorithms for invasive and expansive species classification using airborne hyperspectral data. Remote Sens., 12.","DOI":"10.3390\/rs12030516"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1080\/2150704X.2019.1649736","article-title":"Hyperspectral image classification based on convolutional neural network and random forest","volume":"10","author":"Wang","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/JSTARS.2018.2809781","article-title":"Cascaded random forest for hyperspectral image classification","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cariou, C., Le Moan, S., and Chehdi, K. (2020). Improving k-nearest neighbor approaches for density-based pixel clustering in hyperspectral remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12223745"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4032","DOI":"10.1109\/JSTARS.2018.2872969","article-title":"KNN-based representation of superpixels for hyperspectral image classification","volume":"11","author":"Tu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6840","DOI":"10.1109\/TGRS.2020.3029578","article-title":"Random subspace-based k-nearest class collaborative representation for hyperspectral image classification","volume":"59","author":"Su","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6440","DOI":"10.1109\/TGRS.2018.2838665","article-title":"Active learning with convolutional neural networks for hyperspectral image classification using a new bayesian approach","volume":"56","author":"Haut","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/LGRS.2017.2780890","article-title":"Semisupervised hyperspectral image classification based on generative adversarial networks","volume":"15","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5046","DOI":"10.1109\/TGRS.2018.2805286","article-title":"Generative adversarial networks for hyperspectral image classification","volume":"56","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, H., Tao, C., Qi, J., Li, H., and Tang, Y. (August, January 28). Semi-supervised variational generative adversarial networks for hyperspectral image classification. Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900073"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2016.2636241","article-title":"Deep recurrent neural networks for hyperspectral image classification","volume":"55","author":"Mou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4141","DOI":"10.1109\/JSTARS.2018.2844873","article-title":"Spatial sequential recurrent neural network for hyperspectral image classification","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5384","DOI":"10.1109\/TGRS.2019.2899129","article-title":"Cascaded recurrent neural networks for hyperspectral image classification","volume":"57","author":"Hang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5585","DOI":"10.1109\/TGRS.2017.2710079","article-title":"Deep fully convolutional network-based spatial distribution prediction for hyperspectral image classification","volume":"55","author":"Jiao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1109\/LGRS.2017.2786272","article-title":"Classification of hyperspectral imagery using a new fully convolutional neural network","volume":"15","author":"Li","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1109\/JSTARS.2020.2968179","article-title":"Spectral\u2013spatial exploration for hyperspectral image classification via the fusion of fully convolutional networks","volume":"13","author":"Zou","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xu, H., Yao, W., Cheng, L., and Li, B. (2021). Multiple spectral resolution 3D convolutional neural network for hyperspectral image classification. Remote Sens., 13.","DOI":"10.3390\/rs13071248"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Qing, Y., and Liu, W. (2021). Hyperspectral image classification based on multi-Scale residual network with attention mechanism. Remote Sens., 13.","DOI":"10.3390\/rs13030335"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rao, M., Tang, P., and Zhang, Z. (2020). A developed siamese CNN with 3D adaptive spatial-spectral pyramid pooling for hyperspectral image classification. Remote Sens., 12.","DOI":"10.3390\/rs12121964"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Miclea, A.V., Terebes, R., and Meza, S. (2020, January 21\u201323). One dimensional convolutional neural networks and local binary patterns for hyperspectral image classification. Proceedings of the 2020 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), Cluj-Napoca, Romania.","DOI":"10.1109\/AQTR49680.2020.9129920"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4604","DOI":"10.1109\/TGRS.2020.2964627","article-title":"Hyperspectral image classification with convolutional neural network and active learning","volume":"58","author":"Cao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., and Shen, Q. (2017). Spectral\u2013spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens., 9.","DOI":"10.3390\/rs9010067"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ma, W., Yang, Q., Wu, Y., Zhao, W., and Zhang, X. (2019). Double-branch multi-attention mechanism network for hyperspectral image classification. Remote Sens., 11.","DOI":"10.3390\/rs11111307"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, R., Zheng, S., Duan, C., Yang, Y., and Wang, X. (2020). Classification of hyperspectral image based on double-branch dual-attention mechanism network. Remote Sens., 12.","DOI":"10.20944\/preprints201912.0059.v2"},{"key":"ref_36","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Li, J., Ma, L., Jiang, H., and Zhao, H. (2017, January 23\u201328). Deep residual networks for hyperspectral image classification. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127330"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral\u2013spatial residual network for hyperspectral image classification: A 3-D deep learning framework","volume":"56","author":"Zhong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Meng, Z., Li, L., Tang, X., Feng, Z., Jiao, L., and Liang, M. (2019). Multipath residual network for spectral-spatial hyperspectral image classification. Remote Sens., 11.","DOI":"10.3390\/rs11161896"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Han, D., Kim, J., and Kim, J. (2017, January 21\u201326). Deep pyramidal residual networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.668"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TGRS.2018.2860125","article-title":"Deep pyramidal residual networks for spectral\u2013spatial hyperspectral image classification","volume":"57","author":"Paoletti","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Meng, Z., Li, L., Jiao, L., Feng, Z., Tang, X., and Liang, M. (2019). Fully dense multiscale fusion network for hyperspectral image classification. Remote Sens., 11.","DOI":"10.3390\/rs11222718"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.1109\/JSTARS.2021.3053567","article-title":"Hyperspectral image classification with mixed link networks","volume":"14","author":"Meng","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.3390\/rs10091454","article-title":"Deep&dense convolutional neural network for hyperspectral image classification","volume":"10","author":"Paoletti","year":"2018","journal-title":"Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"102994","DOI":"10.1016\/j.micpro.2020.102994","article-title":"Towards resource-frugal deep convolutional neural networks for hyperspectral image segmentation","volume":"73","author":"Nalepa","year":"2020","journal-title":"Microprocess. Microsyst."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Fu, P., Sun, X., and Sun, Q. (2017). Hyperspectral image segmentation via frequency-based similarity for mixed noise estimation. Remote Sens., 9.","DOI":"10.3390\/rs9121237"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2810","DOI":"10.1109\/TIP.2021.3055613","article-title":"A supervised segmentation network for hyperspectral image classification","volume":"30","author":"Sun","year":"2021","journal-title":"IEEE Trans. Image Process"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Si, Y., Gong, D., Guo, Y., Zhu, X., Huang, Q., Evans, J., He, S., and Sun, Y. (2021). An Advanced Spectral\u2013Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+. Appl. Sci., 11.","DOI":"10.3390\/app11125703"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Takahashi, N., and Mitsufuji, Y. (2020). Densely connected multidilated convolutional networks for dense prediction tasks. arXiv.","DOI":"10.1109\/CVPR46437.2021.00105"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Meng, Z., Zhao, F., Liang, M., and Xie, W. (2021). Deep Residual Involution Network for Hyperspectral Image Classification. Remote Sens., 13.","DOI":"10.3390\/rs13163055"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Shi, H., Cao, G., Ge, Z., Zhang, Y., and Fu, P. (2021). Double-Branch Network with Pyramidal Convolution and Iterative Attention for Hyperspectral Image Classification. Remote Sens., 13.","DOI":"10.3390\/rs13071403"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Gong, H., Li, Q., Li, C., Dai, H., He, Z., Wang, W., Li, H., Han, F., Tuniyazi, A., and Mu, T. (2021). Multiscale Information Fusion for Hyperspectral Image Classification Based on Hybrid 2D-3D CNN. Remote Sens., 13.","DOI":"10.3390\/rs13122268"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, C., Qiu, Z., Cao, X., Chen, Z., Gao, H., and Hua, Z. (2021). Hybrid Dilated Convolution with Multi-Scale Residual Fusion Network for Hyperspectral Image Classification. Micromachines, 12.","DOI":"10.3390\/mi12050545"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5760","DOI":"10.1109\/JSTARS.2021.3083283","article-title":"Hierarchical Shrinkage Multi-Scale Network for Hyperspectral Image Classification with Hierarchical Feature Fusion","volume":"14","author":"Gao","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3396\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:52:53Z","timestamp":1760165573000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,26]]},"references-count":56,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13173396"],"URL":"https:\/\/doi.org\/10.3390\/rs13173396","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,26]]}}}