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Sensing"],"abstract":"<jats:p>Hyperspectral Remote Rensing Image (HRSI) classification based on Convolution Neural Network (CNN) has become one of the hot topics in the field of remote sensing. However, the high dimensional information and limited training samples are prone to the Hughes phenomenon for hyperspectral remote sensing images. Meanwhile, high-dimensional information processing also consumes significant time and computing power, or the extracted features may not be representative, resulting in unsatisfactory classification efficiency and accuracy. To solve these problems, an attention mechanism and depthwise separable convolution are introduced to the three-dimensional convolutional neural network (3DCNN). Thus, 3DCNN-AM and 3DCNN-AM-DSC are proposed for HRSI classification. Firstly, three hyperspectral datasets (Indian pines, University of Pavia and University of Houston) are used to analyze the patchsize and dataset allocation ratio (Training set: Validation set: Test Set) in the performance of 3DCNN and 3DCNN-AM. Secondly, in order to improve work efficiency, principal component analysis (PCA) and autoencoder (AE) dimension reduction methods are applied to reduce data dimensionality, and maximize the classification accuracy of the 3DCNN, but it will still take time. Furthermore, the HRSI classification model 3DCNN-AM and 3DCNN-AM-DSC are applied to classify with the three classic HRSI datasets. Lastly, the classification accuracy index and time consumption are evaluated. The results indicate that 3DCNN-AM could improve classification accuracy and reduce computing time with the dimension reduction dataset, and the 3DCNN-AM-DSC model can reduce the training time by a maximum of 91.77% without greatly reducing the classification accuracy. The results of the three classic hyperspectral datasets illustrate that 3DCNN-AM-DSC can improve the classification performance and reduce the time required for model training. It may be a new way to tackle hyperspectral datasets in HRSl classification tasks without dimensionality reduction.<\/jats:p>","DOI":"10.3390\/rs14092215","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T02:46:39Z","timestamp":1651805199000},"page":"2215","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1108-0507","authenticated-orcid":false,"given":"Wenmei","family":"Li","sequence":"first","affiliation":[{"name":"School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"},{"name":"Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5816-7079","authenticated-orcid":false,"given":"Huaihuai","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]},{"given":"Qing","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]},{"given":"Haiyan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3888-2881","authenticated-orcid":false,"given":"Guan","family":"Gui","sequence":"additional","affiliation":[{"name":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TGRS.2011.2162339","article-title":"On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification","volume":"50","author":"Zhang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.isprsjprs.2021.02.007","article-title":"Automatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network","volume":"174","author":"Sun","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shan, X., Liu, P., Wang, Y., Zhou, Q., and Wang, Z. (2021). Deep Hashing Using Proxy Loss on Remote Sensing Image Retrieval. Remote Sens., 13.","DOI":"10.3390\/rs13152924"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2304","DOI":"10.1109\/JSTARS.2020.2994334","article-title":"Reconstruction of Hyperspectral Images From Spectral Compressed Sensing Based on a Multitype Mixing Model","volume":"13","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1007\/s12665-011-1112-y","article-title":"Application of hyperspectral remote sensing for environment monitoring in mining areas","volume":"65","author":"Zhang","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Freitas, S., Silva, H., and Silva, E. (2021). Remote Hyperspectral Imaging Acquisition and Characterization for Marine Litter Detection. Remote Sens., 13.","DOI":"10.3390\/rs13132536"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/JSTARS.2013.2267204","article-title":"Progress in Hyperspectral Remote Sensing Science and Technology in China Over the Past Three Decades","volume":"7","author":"Tong","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"10348","DOI":"10.1109\/TGRS.2020.3045273","article-title":"Hyperspectral Image Denoising Using a 3-D Attention Denoising Network","volume":"59","author":"Shi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1109\/JSTARS.2020.3011992","article-title":"3-D Channel and Spatial Attention Based Multiscale Spatial\u2013Spectral Residual Network for Hyperspectral Image Classification","volume":"13","author":"Lu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","unstructured":"Wechsler, H. (1992). III.3\u2014Theory of the Backpropagation Neural Network. Neural Networks for Perception, Academic Press."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/MGRS.2019.2911100","article-title":"Hyperspectral Band Selection: A Review","volume":"7","author":"Sun","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/JSTARS.2013.2282166","article-title":"Classification of Australian Native Forest Species Using Hyperspectral Remote Sensing and Machine-Learning Classification Algorithms","volume":"7","author":"Shang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"012087","DOI":"10.1088\/1742-6596\/1950\/1\/012087","article-title":"Hyperspectral Image Classification Using Deep Learning Models: A Review","volume":"1950","author":"Kumar","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5408","DOI":"10.1109\/TGRS.2018.2815613","article-title":"Hyperspectral Image Classification with Deep Learning Models","volume":"56","author":"Yang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jia, S., Jiang, S., Lin, Z., Li, N., Xu, M., and Yu, S. (2021). A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples. arXiv.","DOI":"10.1016\/j.neucom.2021.03.035"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1109\/LGRS.2007.900751","article-title":"Modified Fisher\u2019s Linear Discriminant Analysis for Hyperspectral Imagery","volume":"4","author":"Du","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","first-page":"224","article-title":"Classification of high spatial resolution remote sensing imagery based on object-oriented multi-scale weighted sparse representation","volume":"51","author":"Liang","year":"2022","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_21","first-page":"40","article-title":"Multiscale filter-based hyperspectral image classification with PCA and SVM","volume":"72","author":"Chen","year":"2021","journal-title":"J. Electr. Eng."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","unstructured":"Din\u00e7, S., and Ayg\u00fcn, R.S. (2013). Evaluation of Hyperspectral Image Classification Using Random Forest and Fukunaga-Koontz Transform. Machine Learning and Data Mining in Pattern Recognition, Springer.","DOI":"10.1007\/978-3-642-39712-7_18"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hsieh, T.H., and Kiang, J.F. (2020). Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands. Sensors, 20.","DOI":"10.3390\/s20061734"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.patcog.2017.09.007","article-title":"Superpixel-based 3D deep neural networks for hyperspectral image classification","volume":"74","author":"Shi","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_29","first-page":"016005","article-title":"Spectral\u2013spatial classification of hyperspectral image using three-dimensional convolution network","volume":"12","author":"Liu","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1109\/LGRS.2017.2668299","article-title":"Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network","volume":"14","author":"Palsson","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"026508","DOI":"10.1117\/1.JRS.13.026508","article-title":"Fusion of heterogeneous bands and kernels in hyperspectral image processing","volume":"13","author":"Islam","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1117\/1.OE.51.11.111704","article-title":"Target detection of hyperspectral images based on their Fourier spectral features","volume":"51","author":"Saipullah","year":"2012","journal-title":"Opt. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"012182","DOI":"10.1088\/1742-6596\/1693\/1\/012182","article-title":"Hyperspectral Image Dimension Reduction and Target Detection Based on Weighted Mean Filter and Manifold Learning","volume":"1693","author":"Jiang","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Marotz, J., Kulcke, A., Siemers, F., Cruz, D., Aljowder, A., Promny, D., Daeschlein, G., and Wild, T. (2019). Extended Perfusion Parameter Estimation from Hyperspectral Imaging Data for Bedside Diagnostic in Medicine. Molecules, 24.","DOI":"10.3390\/molecules24224164"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gajbhiye, A., Jaf, S., Moubayed, N.A., Bradley, S., and McGough, A.S. (2018, January 10\u201313). CAM: A Combined Attention Model for Natural Language Inference. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622057"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xu, R., Tao, Y., Lu, Z., and Zhong, Y. (2018). Attention-Mechanism-Containing Neural Networks for High-Resolution Remote Sensing Image Classification. Remote Sens., 10.","DOI":"10.3390\/rs10101602"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.neucom.2018.01.007","article-title":"Fine-grained attention mechanism for neural machine translation","volume":"284","author":"Choi","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1109\/TMM.2017.2648498","article-title":"Diversified Visual Attention Networks for Fine-Grained Object Classification","volume":"19","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_39","first-page":"1","article-title":"S\u00b3Net: Spectral\u2013Spatial\u2013Semantic Network for Hyperspectral Image Classification with the Multiway Attention Mechanism","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s00530-003-0105-4","article-title":"A visual attention model for adapting images on small displays","volume":"9","author":"Chen","year":"2003","journal-title":"Multimed. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1109\/TBC.2021.3099737","article-title":"Light Field Image Quality Assessment with Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions","volume":"67","author":"Qu","year":"2021","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_42","unstructured":"Sun, F., Liu, H., and Hu, D. (2019). Depthwise Separable Convolution Feature Learning for Ihomogeneous Rock Image Classification. Cognitive Systems and Signal Processing, Springer."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hoang, V.T., Hoang, V.D., and Jo, K.H. (2020, January 14\u201315). Realtime Multi-Person Pose Estimation with RCNN and Depthwise Separable Convolution. Proceedings of the 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), Ho Chi Minh City, Vietnam.","DOI":"10.1109\/RIVF48685.2020.9140731"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lu, Y., Shan, W., and Xu, J. (2019, January 11\u201314). A Depthwise Separable Convolution Neural Network for Small-footprint Keyword Spotting Using Approximate MAC Unit and Streaming Convolution Reuse. Proceedings of the 2019 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Bangkok, Thailand.","DOI":"10.1109\/APCCAS47518.2019.8953096"},{"key":"ref_45","unstructured":"Morales, A., Fierrez, J., S\u00e1nchez, J.S., and Ribeiro, B. (2019). An Improvement for Capsule Networks Using Depthwise Separable Convolution. Pattern Recognition and Image Analysis, Springer International Publishing."},{"key":"ref_46","first-page":"1415","article-title":"A CNN Accelerator on FPGA Using Depthwise Separable Convolution","volume":"65","author":"Bai","year":"2018","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hu, G., Wang, K., and Liu, L. (2021). Underwater Acoustic Target Recognition Based on Depthwise Separable Convolution Neural Networks. Sensors, 21.","DOI":"10.3390\/s21041429"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Raczko, E., and Zagajewski, B. (2018). Tree Species Classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) Using Artificial Neural Networks and APEX Hyperspectral Images. Remote Sens., 10.","DOI":"10.3390\/rs10071111"},{"key":"ref_49","first-page":"art00009","article-title":"Spectral Resolution Enhancement of Hyperspectral Images via Sparse Representations","volume":"2016","author":"Fotiadou","year":"2016","journal-title":"Electron. Imaging"},{"key":"ref_50","unstructured":"Sun, X., Zhang, X., Xia, Z., and Bertino, E. (2021). Research and Implementation of Dimension Reduction Algorithm in Big Data Analysis. Artificial Intelligence and Security, Springer International Publishing."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1117\/1.1637907","article-title":"Feature reduction of hyperspectral imagery using hybrid wavelet-principal component analysis","volume":"43","author":"Kaewpijit","year":"2004","journal-title":"Opt. Eng."},{"key":"ref_52","unstructured":"Lin, Z., Chen, Y., Zhao, X., and Wang, G. (2013, January 10\u201313). Spectral-spatial classification of hyperspectral image using autoencoders. Proceedings of the 2013 9th International Conference on Information, Communications Signal Processing, Tainan, Taiwan."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1007\/s11263-020-01302-5","article-title":"Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in CNNs","volume":"128","author":"Hackel","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Gao, H., Liu, X., Qu, M., and Huang, S. (2021). PDANet: Self-Supervised Monocular Depth Estimation Using Perceptual and Data Augmentation Consistency. Appl. Sci., 11.","DOI":"10.3390\/app11125383"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"42384","DOI":"10.1109\/ACCESS.2020.2977454","article-title":"Hyperspectral Band Selection Using Attention-Based Convolutional Neural Networks","volume":"8","author":"Tulczyjew","year":"2020","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"114528","DOI":"10.1016\/j.eswa.2020.114528","article-title":"CNN with depthwise separable convolutions and combined kernels for rating prediction","volume":"170","author":"Khan","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3279","DOI":"10.1109\/TCSI.2021.3078541","article-title":"Dynamic Dataflow Scheduling and Computation Mapping Techniques for Efficient Depthwise Separable Convolution Acceleration","volume":"68","author":"Li","year":"2021","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"103038","DOI":"10.1016\/j.jvcir.2021.103038","article-title":"Real-time video super-resolution using lightweight depthwise separable group convolutions with channel shuffling","volume":"75","author":"Xiao","year":"2021","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Ma, H., Liu, G., and Yuan, Y. (2020, January 4\u20138). Enhanced Non-Local Cascading Network with Attention Mechanism for Hyperspectral Image Denoising. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9054630"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Vuolo, F., Berger, K., and Atzberger, C. (2011). Evaluation of time-series and phenological indicators for land cover classification based on MODIS data. Process. SPIE Int. Soc. Opt. Eng., 8174.","DOI":"10.1117\/12.898389"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TPAMI.2009.187","article-title":"Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation","volume":"32","author":"Lozano","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_62","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_63","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/LGRS.2019.2924059","article-title":"Semisupervised Hyperspectral Image Classification with Cluster-Based Conditional Generative Adversarial Net","volume":"17","author":"Zhao","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1080\/2150704X.2017.1335902","article-title":"Classification of hyperspectral and LIDAR data using extinction profiles with feature fusion","volume":"8","author":"Zhang","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2004.06.017","article-title":"Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification","volume":"93","author":"Foody","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/TGRS.2019.2933609","article-title":"Learning to Pay Attention on Spectral Domain: A Spectral Attention Module-Based Convolutional Network for Hyperspectral Image Classification","volume":"58","author":"Mou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TGRS.2019.2934760","article-title":"HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation from Transformers","volume":"58","author":"He","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3006","DOI":"10.1109\/JSTARS.2021.3062872","article-title":"Sandwich Convolutional Neural Network for Hyperspectral Image Classification Using Spectral Feature Enhancement","volume":"14","author":"Gao","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5609","DOI":"10.1109\/JSTARS.2020.3023483","article-title":"Sparse and Low-Rank Representation with Key Connectivity for Hyperspectral Image Classification","volume":"13","author":"Ding","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1109\/LGRS.2018.2871273","article-title":"Sparse Representation-Based Hyperspectral Image Classification Using Multiscale Superpixels and Guided Filter","volume":"16","author":"Dundar","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3215","DOI":"10.1109\/JSTARS.2021.3063335","article-title":"Self-Supervised Deep Subspace Clustering for Hyperspectral Images with Adaptive Self-Expressive Coefficient Matrix Initialization","volume":"14","author":"Li","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"4939","DOI":"10.1109\/TGRS.2020.2969024","article-title":"Classification of Hyperspectral and LiDAR Data Using Coupled CNNs","volume":"58","author":"Hang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2215\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:06:32Z","timestamp":1760137592000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2215"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,5]]},"references-count":72,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092215"],"URL":"https:\/\/doi.org\/10.3390\/rs14092215","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,5]]}}}