{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T17:57:35Z","timestamp":1783360655897,"version":"3.54.6"},"reference-count":47,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T00:00:00Z","timestamp":1687132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["51663001"],"award-info":[{"award-number":["51663001"]}]},{"name":"National Natural Science Foundation of China","award":["52063002"],"award-info":[{"award-number":["52063002"]}]},{"name":"National Natural Science Foundation of China","award":["42061067"],"award-info":[{"award-number":["42061067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Traditional hyperspectral image semantic segmentation algorithms can not fully utilize the spatial information or realize efficient segmentation with less sample data. In order to solve the above problems, a U-shaped hyperspectral semantic segmentation model (DCCaps-UNet) based on the depthwise separable and conditional convolution capsule network was proposed in this study. The whole network is an encoding\u2013decoding structure. In the encoding part, image features are firstly fully extracted and fused. In the decoding part, images are then reconstructed by upsampling. In the encoding part, a dilated convolutional capsule block is proposed to fully acquire spatial information and deep features and reduce the calculation cost of dynamic routes using a conditional sliding window. A depthwise separable block is constructed to replace the common convolution layer in the traditional capsule network and efficiently reduce network parameters. After principal component analysis (PCA) dimension reduction and patch preprocessing, the proposed model was experimentally tested with Indian Pines and Pavia University public hyperspectral image datasets. The obtained segmentation results of various ground objects were analyzed and compared with those obtained with other semantic segmentation models. The proposed model performed better than other semantic segmentation methods and achieved higher segmentation accuracy with the same samples. Dice coefficients reached 0.9989 and 0.9999. The OA value can reach 99.92% and 100%, respectively, thus, verifying the effectiveness of the proposed model.<\/jats:p>","DOI":"10.3390\/rs15123177","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T02:29:19Z","timestamp":1687141759000},"page":"3177","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["DCCaps-UNet: A U-Shaped Hyperspectral Semantic Segmentation Model Based on the Depthwise Separable and Conditional Convolution Capsule Network"],"prefix":"10.3390","volume":"15","author":[{"given":"Siqi","family":"Wei","sequence":"first","affiliation":[{"name":"College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yafei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4175-088X","authenticated-orcid":false,"given":"Mengshan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haijun","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixin","family":"Guan","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.1016\/j.rse.2007.12.014","article-title":"Three decades of hyperspectral remote sensing of the earth: A personal view","volume":"113","author":"Goetz","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"307","DOI":"10.14358\/PERS.83.4.307","article-title":"Multilayer nmf for blind unmixing of hyperspectral imagery with additional constraints","volume":"83","author":"Chen","year":"2017","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1080\/10106049.2017.1408701","article-title":"A local mahalanobis-distance method based on tensor decomposition for hyperspectral anomaly detection","volume":"34","author":"Zhao","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, C., Wang, Y., Zhang, X., Gao, H., Yang, Y., and Wang, J. (2019). Deep belief network for spectral-spatial classification of hyperspectral remote sensor data. Sensors, 19.","DOI":"10.3390\/s19010204"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1007\/s40010-017-0433-y","article-title":"A research review on hyperspectral data processing and analysis algorithms","volume":"87","author":"Kale","year":"2017","journal-title":"Proc. Natl. Acad. Sci. India Sect. A-Phys. Sci."},{"key":"ref_6","first-page":"145","article-title":"A review of hyperspectral remote sensing and its application in vegetation and water resource studies","volume":"33","author":"Govender","year":"2007","journal-title":"Water Sa"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Terentev, A., Dolzhenko, V., Fedotov, A., and Eremenko, D. (2022). Current state of hyperspectral remote sensing for early plant disease detection: A review. Sensors, 22.","DOI":"10.3390\/s22030757"},{"key":"ref_8","first-page":"1","article-title":"Small waterbody extraction with improved u-net using zhuhai-1 hyperspectral remote sensing images","volume":"19","author":"Qin","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hennessy, A., Clarke, K., and Lewis, M. (2021). Generative adversarial network synthesis of hyperspectral vegetation data. Remote Sens., 13.","DOI":"10.3390\/rs13122243"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., and Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.biosystemseng.2020.09.002","article-title":"Application of non-linear partial least squares analysis on prediction of biomass of maize plants using hyperspectral images","volume":"200","author":"Ma","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105222","DOI":"10.1016\/j.catena.2021.105222","article-title":"Hyperspectral inversion of soil heavy metals in three-river source region based on random forest model","volume":"202","author":"Zhou","year":"2021","journal-title":"Catena"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4481","DOI":"10.1109\/TIM.2018.2887069","article-title":"Measurement, Medical hyperspectral image classification based on end-to-end fusion deep neural network","volume":"68","author":"Wei","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cui, R., Yu, H., Xu, T., Xing, X., Cao, X., Yan, K., and Chen, J. (2022). Deep learning in medical hyperspectral images: A review. Sensors, 22.","DOI":"10.3390\/s22249790"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"34027","DOI":"10.1007\/s11042-019-08114-x","article-title":"Hyper-spectral image segmentation using an improved pso aided with multilevel fuzzy entropy","volume":"78","author":"Chakraborty","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ismail, M.J.A. (2020). Segment-based clustering of hyperspectral images using tree-based data partitioning structures. Algorithms, 13.","DOI":"10.3390\/a13120330"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101","DOI":"10.5566\/ias.v26.p101-109","article-title":"Morphological segmentation of hyperspectral images","volume":"26","author":"Noyel","year":"2007","journal-title":"Image Anal. Ster."},{"key":"ref_18","unstructured":"Mercier, G., Derrode, S., and Lennon, M. (2003, January 21\u201325). Hyperspectral image segmentation with markov chain model. Proceedings of the IEEE International Geoscience & Remote Sensing Symposium 2003, Toulouse, France."},{"key":"ref_19","unstructured":"Acito, N., Corsini, G., and Diani, M. (2003, January 21\u201325). An unsupervised algorithm for hyperspectral image segmentation based on the gaussian mixture model. Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium, (IGARSS \u201903), Toulouse, France."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3947","DOI":"10.1109\/TGRS.2011.2128330","article-title":"Hyperspectral image segmentation using a new bayesian approach with active learning","volume":"49","author":"Li","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TGRS.2011.2162649","article-title":"Spectral-spatial 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_22","first-page":"533","article-title":"Application of semantic segmentation based on convolutional neural network in medical images","volume":"37","author":"Wu","year":"2020","journal-title":"Sheng Wu Yi Xue Gong Cheng Xue Za Zhi J. Biomed. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"051101","DOI":"10.1007\/s11432-017-9189-6","article-title":"Survey of recent progress in semantic image segmentation with cnns","volume":"61","author":"Geng","year":"2018","journal-title":"Sci. China-Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"258619","DOI":"10.1155\/2015\/258619","article-title":"Deep convolutional neural networks for hyperspectral image classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_25","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 Geoscience & Remote Sensing Symposium 2015, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.1109\/TGRS.2018.2794326","article-title":"Hyperspectral image classification with deep feature fusion network","volume":"56","author":"Song","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/TIP.2017.2725580","article-title":"Going deeper with contextual cnn for hyperspectral image classification","volume":"26","author":"Lee","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1109\/TGRS.2017.2769673","article-title":"Supervised deep feature extraction for hyperspectral image classification","volume":"56","author":"Liu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2354","DOI":"10.1109\/TIP.2018.2799324","article-title":"Hyperspectral image classification with markov random fields and a convolutional neural network","volume":"27","author":"Cao","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7803","DOI":"10.1109\/TGRS.2020.3038425","article-title":"Automatic clustering-based two-branch cnn for hyperspectral image classification","volume":"59","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"67","DOI":"10.3390\/rs9010067","article-title":"Spectral\u2013spatial classification of hyperspectral imagery with 3d convolutional neural network","volume":"9","author":"Ying","year":"2017","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xu, Q., Xiao, Y., Wang, D., and Luo, B.J.R.S. (2020). Csa-mso3dcnn: Multiscale octave 3d cnn with channel and spatial attention for hyperspectral image classification. Remote Sens., 12.","DOI":"10.3390\/rs12010188"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7570","DOI":"10.1109\/JSTARS.2021.3099118","article-title":"Hyperspectral image classification using a hybrid 3d-2d convolutional neural networks","volume":"14","author":"Ghaderizadeh","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1109\/LGRS.2019.2895697","article-title":"Validating hyperspectral image segmentation","volume":"16","author":"Nalepa","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","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_37","unstructured":"Sabour, S., Frosst, N., and Hinton, G.E. (2017). Dynamic routing between capsules. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sun, L., Song, X., Guo, H., Zhao, G., and Wang, J. (2021). Patch-wise semantic segmentation for hyperspectral images via a cubic capsule network with emap features. Remote Sens., 13.","DOI":"10.3390\/rs13173497"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015). Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Cham, Switzerland, 18 November 2015, Springer International Publishing.","DOI":"10.1007\/978-3-319-24553-9"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, H., Zhang, A., and Liu, Y. (2022). Semantic segmentation of hyperspectral remote sensing images based on PSE-UNet model. Sensors, 22.","DOI":"10.3390\/s22249678"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s40537-023-00718-3","article-title":"CEU-Net: Ensemble semantic segmentation of hyperspectral images using clustering","volume":"10","author":"Soucy","year":"2023","journal-title":"J. Big Data"},{"key":"ref_43","unstructured":"Baumgardner, M.F., Biehl, L.L., and Landgrebe, D.A. (2015). 220 Band Aviris Hyperspectral Image Data Set: June 12, 1992 Indian Pine Test Site 3, Purdue University Research Repository."},{"key":"ref_44","unstructured":"(2023, June 16). University of Pavia Dataset. Available online: https:\/\/www.ehu.eus\/ccwintco\/index.php\/Hyperspectral_Remote_Sensing_Scenes#Pavia_University_scene."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","article-title":"Hybridsn: Exploring 3-d\u20132-d cnn feature hierarchy for hyperspectral image classification","volume":"17","author":"Roy","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","unstructured":"Chakraborty, T., and Trehan, U. (2021). Spectralnet: Exploring spatial-spectral waveletcnn for hyperspectral image classification. arXiv."},{"key":"ref_47","unstructured":"Jaime Moraga, H.S.D. (2022). Jigsawhsi: A network for hyperspectral image classification. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3177\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:56:11Z","timestamp":1760126171000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,19]]},"references-count":47,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15123177"],"URL":"https:\/\/doi.org\/10.3390\/rs15123177","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,19]]}}}