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Hyperspectral remote sensing images (HSI) provide spectral information and influence LCC. Convolutional neural networks (CNNs) improve the performance of hyperspectral image classification with their powerful feature learning ability. However, if pixel-wise spectra are used as inputs to CNNs, they are ineffective in solving spatial relationships. To address the issue of insufficient spatial information in CNNs, capsule networks adopt a vector to represent position transformation information. Herein, we combine a clustering-based band selection method and residual and capsule networks to create a deep model named ResCapsNet. We tested the robustness of ResCapsNet using Gaofen-5 Imagery. The images covered two heterogeneous study areas in Wuhan City and Xinjiang Province, with spatially weakly dependent and spatially basically independent datasets, respectively. Compared with other methods, the model achieved the best performances, with averaged overall accuracies of 98.45 and 82.80% for Wuhan study area, and 92.82 and 70.88% for Xinjiang study area. Four transfer learning methods were investigated for cross-training and prediction of those two areas and achieved good results. In summary, the proposed model can effectively improve the classification accuracy of HSI in heterogeneous environments.<\/jats:p>","DOI":"10.3390\/rs14133216","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T20:59:18Z","timestamp":1656968358000},"page":"3216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Classification of Heterogeneous Mining Areas Based on ResCapsNet and Gaofen-5 Imagery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7201-9208","authenticated-orcid":false,"given":"Renxiang","family":"Guan","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihao","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teng","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7785-2541","authenticated-orcid":false,"given":"Xianju","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinzhong","family":"Yang","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6272-1618","authenticated-orcid":false,"given":"Weitao","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1080\/13658816.2017.1324976","article-title":"Classifying urban land use by integrating remote sensing and social media data","volume":"31","author":"Liu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.isprsjprs.2020.06.014","article-title":"X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data","volume":"167","author":"Hong","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, J., Xiang, J., Jin, Y., Liu, R., Yan, J., and Wang, L. (2021). Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey. Remote Sens., 13.","DOI":"10.3390\/rs13214387"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1109\/JSTARS.2022.3141826","article-title":"GCSANet: A Global Context Spatial Attention Deep Learning Network for Remote Sensing Scene Classification","volume":"15","author":"Chen","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","first-page":"1","article-title":"Split Depth-Wise Separable Graph-Convolution Network for Road Extraction in Complex Environments from High-Resolution Remote-Sensing Images","volume":"60","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4121","DOI":"10.1109\/JSTARS.2020.3009352","article-title":"Channel-Attention-Based DenseNet Network for Remote Sensing Image Scene Classification","volume":"13","author":"Tong","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, T., Hu, N., Niu, R., Zhen, N., and Plaza, A. (2020). Object-Oriented Open-Pit Mine Mapping Using Gaofen-2 Satellite Image and Convolutional Neural Network, for the Yuzhou City, China. Remote Sens., 12.","DOI":"10.3390\/rs12233895"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1080\/1747423X.2010.500688","article-title":"Social and ecological factors and land-use land-cover diversity in two provinces in Southeast Asia","volume":"5","author":"Cassidy","year":"2010","journal-title":"J. Land Use Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Azeez, N., Yahya, W., Al-Taie, I., Basbrain, A., and Clark, A. (2019). Regional Agricultural Land Classification Based on Random Forest (RF), Decision Tree, and SVMs Techniques, ICICT.","DOI":"10.1007\/978-981-15-0637-6_6"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, W., Cheng, X., and Wang, L. (2016). A comparison of machine learning algorithms for mapping of complex surface-mined and agricultural landscapes using ziyuan-3 stereo satellite imagery. Remote Sens., 8.","DOI":"10.3390\/rs8060514"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, W., Li, X., and Wang, L. (2020). Fine Land Cover Classification in an Open Pit Mining Area Using Optimized Support Vector Machine and WorldView-3 Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12010082"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, W., Li, X., He, H., and Wang, L. (2018). A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques. Remote Sens., 10.","DOI":"10.3390\/rs10010015"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, M., Tang, Z., Tong, W., Li, X., Chen, W., and Wang, L. (2021). A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery. Sensors, 21.","DOI":"10.3390\/s21062089"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Qian, M., Sun, S., and Li, X. (2021). Multimodal Data and Multiscale Kernel-Based Multistream CNN for Fine Classification of a Complex Surface-Mined Area. Remote Sens., 13.","DOI":"10.3390\/rs13245052"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"Roweis","year":"2000","journal-title":"Science"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Burger, J.E., and Geladi, P.L.M. (2007). Hyperspectral Image Data Conditioning and Regression Analysis. Techniques and Applications of Hyperspectral Image Analysis, Wiley.","DOI":"10.1002\/9780470010884.ch6"},{"key":"ref_18","first-page":"112","article-title":"Multi- and hyperspectral geologic remote sensing: A review","volume":"14","author":"Hecker","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J.J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MGRS.2017.2762087","article-title":"Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_21","unstructured":"Wu, J., Ke, C., Cai, Y., and Duan, Z. (2022). Monitoring multi-temporal changes of lakes on the tibetan plateau using multi-source remote sensing data from 1992 to 2019: A case study of lake Zhari Namco. J. Earth Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/TGRS.2008.2005729","article-title":"Classification of Hyperspectral Images with Regularized Linear Discriminant Analysis","volume":"47","author":"Bandos","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/LGRS.2011.2172185","article-title":"Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles","volume":"9","author":"Licciardi","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3185","DOI":"10.1109\/TGRS.2018.2794443","article-title":"Graph-regularized fast and robust principal component analysis for hyperspectral band selection","volume":"56","author":"Sun","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xie, F., Li, F., Lei, C., and Ke, L. (2018). Representative band selection for hyperspectral image classification. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7090338"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1049\/iet-cvi.2009.0034","article-title":"Band Selection for Hyperspectral Imagery Using Affinity Propagation","volume":"3","author":"Qian","year":"2009","journal-title":"IET Comput. Vis."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/TGRS.2014.2319373","article-title":"Extended Random Walker-Based Classification of Hyperspectral Images","volume":"53","author":"Kang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"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":"844","DOI":"10.1109\/TGRS.2016.2616355","article-title":"Hyperspectral image classification using deep pixel-pair features","volume":"55","author":"Li","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4729","DOI":"10.1109\/TGRS.2017.2698503","article-title":"Learning and transferring deep joint spectral\u2013spatial features for hyperspectral classification","volume":"55","author":"Yang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6712","DOI":"10.1109\/TGRS.2018.2841823","article-title":"Exploring Hierarchical Convolutional Features for Hyperspectral Image Classification","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","first-page":"1","article-title":"A Supervised Progressive Growing Generative Adversarial Network for Remote Sensing Image Scene Classification","volume":"60","author":"Roy","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3988","DOI":"10.1109\/JSTARS.2021.3069013","article-title":"Enhanced-Random-Feature-Subspace-Based Ensemble CNN for the Imbalanced Hyperspectral Image Classification","volume":"14","author":"Lv","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5612","DOI":"10.1109\/TGRS.2020.2967821","article-title":"FPGA: Fast patch-free global learning framework for fully end-to-end hyperspectral image classification","volume":"58","author":"Zheng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/095400999116340","article-title":"A Recurrent Neural Network that Learns to Count","volume":"11","author":"Rodriguez","year":"1999","journal-title":"Connect. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3516","DOI":"10.1109\/TGRS.2017.2675902","article-title":"Learning to diversify deep belief networks for hyperspectral image classification","volume":"55","author":"Zhong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1109\/JSTARS.2022.3144339","article-title":"JAGAN: A Framework for Complex Land Cover Classification Using Gaofen-5 AHSI Images","volume":"15","author":"Chen","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral\u2013spatial residual network for hyperspectral image classification: A 3D deep learning framework","volume":"56","author":"Zhong","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A., and Doulamis, N. (2015, January 26\u201321). Deep supervised learning for hyperspectral data classification through convolutional neural networks. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref_43","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_44","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS\u201910), Chia Laguna Resort, Sardinia, Italy."},{"key":"ref_45","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, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_46","unstructured":"Sabour, S., Frosst, N., and Hinton, G.E. (2017, January 4\u20139). Dynamic routing between capsules. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.rse.2014.07.004","article-title":"Forested landslide detection using LiDar data and the random forest algorithm: A case study of the Three Gorges, China","volume":"152","author":"Chen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, X., Tan, K., and Chen, Y. (2018, January 18\u201320). CapsNet and Triple-GANs Towards Hyperspectral Classification. Proceedings of the 2018 Fifth International Workshop on Earth Observation and Remote Sensing Applications (EORSA), Xi\u2019an, China.","DOI":"10.1109\/EORSA.2018.8598574"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhu, K., Chen, Y., Ghamisi, P., Jia, X., and Benediktsson, J.A. (2019). Deep convolutional capsule network for hyperspectral image spectral and spectral-spatial classification. Remote Sens., 11.","DOI":"10.3390\/rs11030223"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1109\/TGRS.2018.2871782","article-title":"Capsule Networks for Hyperspectral Image Classification","volume":"57","author":"Paoletti","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1109\/JSTARS.2020.2968930","article-title":"Robust capsule network based on maximum correntropy criterion for hyperspectral image classification","volume":"13","author":"Li","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1109\/LGRS.2019.2891076","article-title":"Hyperspectral Image Classification Using CapsNet with Well-Initialized Shallow Layers","volume":"16","author":"Yin","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Chen, W.T., Li, X.J., He, H.X., and Wang, L.Z. (2018). Assessing Different Feature Sets\u2019 Effects on Land Cover Classification in Complex Surface-Mined Landscapes by ZiYuan-3 Satellite Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10010023"},{"key":"ref_55","first-page":"5910","article-title":"Optimal clustering framework for hyperspectral band selection","volume":"56","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5297","DOI":"10.1109\/JSTARS.2020.3021045","article-title":"Diverse capsules network combining multiconvolutional layers for remote sensing image scene classification","volume":"13","author":"Raza","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, X., Tang, Z., Chen, W., and Wang, L. (2019). Multimodal and Multi-Model Deep Fusion for Fine Classification of Regional Complex Landscape Areas Using ZiYuan-3 Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11222716"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","article-title":"Res2net: A new multi-scale backbone architecture","volume":"43","author":"Gao","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3216\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:42:41Z","timestamp":1760139761000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3216"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,4]]},"references-count":58,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14133216"],"URL":"https:\/\/doi.org\/10.3390\/rs14133216","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,4]]}}}