{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:01:55Z","timestamp":1760144515790,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,27]],"date-time":"2024-04-27T00:00:00Z","timestamp":1714176000000},"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":["62076137","2022r075","2024GXLK19"],"award-info":[{"award-number":["62076137","2022r075","2024GXLK19"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Startup Foundation for Introducing Talent of NUIST","award":["62076137","2022r075","2024GXLK19"],"award-info":[{"award-number":["62076137","2022r075","2024GXLK19"]}]},{"name":"Guangxi Forestry Technology Promotion and Demonstration Application Project","award":["62076137","2022r075","2024GXLK19"],"award-info":[{"award-number":["62076137","2022r075","2024GXLK19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image classification (HSIC) has garnered increasing attention among researchers. While classical networks like convolution neural networks (CNNs) have achieved satisfactory results with the advent of deep learning, they are confined to processing local information. Vision transformers, despite being effective at establishing long-distance dependencies, face challenges in extracting high-representation features for high-dimensional images. In this paper, we present the multiscale efficient attention with enhanced feature transformer (MEA-EFFormer), which is designed for the efficient extraction of spectral\u2013spatial features, leading to effective classification. MEA-EFFormer employs a multiscale efficient attention feature extraction module to initially extract 3D convolution features and applies effective channel attention to refine spectral information. Following this, 2D convolution features are extracted and integrated with local binary pattern (LBP) spatial information to augment their representation. Then, the processed features are fed into a spectral\u2013spatial enhancement attention (SSEA) module that facilitates interactive enhancement of spectral\u2013spatial information across the three dimensions. Finally, these features undergo classification through a transformer encoder. We evaluate MEA-EFFormer against several state-of-the-art methods on three datasets and demonstrate its outstanding HSIC performance.<\/jats:p>","DOI":"10.3390\/rs16091560","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T04:26:16Z","timestamp":1714364776000},"page":"1560","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MEA-EFFormer: Multiscale Efficient Attention with Enhanced Feature Transformer for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2098-9878","authenticated-orcid":false,"given":"Qian","family":"Sun","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9613-9645","authenticated-orcid":false,"given":"Guangrui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3520-2780","authenticated-orcid":false,"given":"Yu","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Chenrong","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin 300072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6465-8678","authenticated-orcid":false,"given":"Le","family":"Sun","sequence":"additional","affiliation":[{"name":"Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Institute of Intelligent Network and Information System, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Xingying","family":"Li","sequence":"additional","affiliation":[{"name":"Guangxi Forest Resources and Environment Monitoring Center, Nanning 530028, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5542116","DOI":"10.1109\/TGRS.2022.3217168","article-title":"SSAU-Net: A Spectral\u2013Spatial Attention-Based U-Net for Hyperspectral Image Fusion","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","first-page":"5519915","article-title":"Large Kernel Spectral and Spatial Attention Networks for Hyperspectral Image Classification","volume":"61","author":"Sun","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"5618314","DOI":"10.1109\/TGRS.2023.3305021","article-title":"CRNet: Channel-enhanced Remodeling-based Network for Salient Object Detection in Optical Remote Sensing Images","volume":"61","author":"Sun","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2100116","DOI":"10.1109\/TGRS.2024.3367374","article-title":"Multiscale 3-D\u20132-D Mixed CNN and Lightweight Attention-Free Transformer for Hyperspectral and LiDAR Classification","volume":"62","author":"Sun","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","first-page":"1","article-title":"MASSFormer: Memory-Augmented Spectral-Spatial Transformer for Hyperspectral Image Classification","volume":"62","author":"Sun","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5505014","DOI":"10.1109\/TGRS.2021.3069845","article-title":"Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing","volume":"60","author":"Yao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2632","DOI":"10.1109\/TGRS.2012.2216272","article-title":"Tree Species Classification in Boreal Forests with Hyperspectral Data","volume":"51","author":"Dalponte","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7789","DOI":"10.1109\/JIOT.2020.3039359","article-title":"Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things","volume":"8","author":"Zhang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7256","DOI":"10.1109\/TIP.2021.3104177","article-title":"Spectral Super-Resolution Network Guided by Intrinsic Properties of Hyperspectral Imagery","volume":"30","author":"Hang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","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_12","unstructured":"Fauvel, M., Chanussot, J., and Benediktsson, J. (2006, January 14\u201319). Evaluation of Kernels for Multiclass Classification of Hyperspectral Remote Sensing Data. Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, Toulouse, France."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.1109\/TGRS.2008.916629","article-title":"Supervised Classification of Remotely Sensed Imagery Using a Modified k-NN Technique","volume":"46","author":"Samaniego","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","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_16","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_17","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1109\/36.628781","article-title":"Image segmentation and discriminant analysis for the identification of land cover units in ecology","volume":"35","author":"Lobo","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8113","DOI":"10.1109\/JSTARS.2021.3103858","article-title":"Background Purification Framework With Extended Morphological Attribute Profile for Hyperspectral Anomaly Detection","volume":"14","author":"Huang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1109\/TNNLS.2020.2978760","article-title":"Naive Gabor Networks for Hyperspectral Image Classification","volume":"32","author":"Liu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jiang, C., and Su, J. (2018, January 7\u201310). Gabor Binary Layer in Convolutional Neural Networks. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451298"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1109\/JSTARS.2014.2360694","article-title":"Hyperspectral Image Classification Using Spectral\u2013Spatial Composite Kernels Discriminant Analysis","volume":"8","author":"Li","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4816","DOI":"10.1109\/TGRS.2012.2230268","article-title":"Generalized Composite Kernel Framework for Hyperspectral Image Classification","volume":"51","author":"Li","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2467","DOI":"10.1109\/TGRS.2014.2360672","article-title":"Hyperspectral Image Classification Based on Three-Dimensional Scattering Wavelet Transform","volume":"53","author":"Tang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep Learning for Hyperspectral Image Classification: An Overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1109\/TGRS.2015.2478379","article-title":"Unsupervised Deep Feature Extraction for Remote Sensing Image Classification","volume":"54","author":"Romero","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","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_27","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_28","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_29","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/LGRS.2018.2830403","article-title":"Deformable Convolutional Neural Networks for Hyperspectral Image Classification","volume":"15","author":"Zhu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4150","DOI":"10.1109\/TGRS.2020.3014313","article-title":"A Lightweight Convolutional Neural Network for Hyperspectral Image Classification","volume":"59","author":"Jia","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1109\/TPAMI.2012.270","article-title":"Schroedinger Eigenmaps for the Analysis of Biomedical Data","volume":"35","author":"Czaja","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3599","DOI":"10.1109\/TGRS.2018.2886022","article-title":"A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image Classification","volume":"57","author":"Gong","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xie, P., Salakhutdinov, R., Mou, L., and Xing, E.P. (2017). Deep Determinantal Point Process for Large-Scale Multi-label Classification.","DOI":"10.1109\/ICCV.2017.59"},{"key":"ref_34","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_35","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_36","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_37","doi-asserted-by":"crossref","first-page":"5624715","DOI":"10.1109\/TGRS.2022.3180068","article-title":"Multiattention Generative Adversarial Network for Remote Sensing Image Super-Resolution","volume":"60","author":"Jia","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Neagoe, V.E., and Diaconescu, P. (2020, January 8\u201320). CNN Hyperspectral Image Classification Using Training Sample Augmentation with Generative Adversarial Networks. Proceedings of the 2020 13th International Conference on Communications (COMM), Bucharest, Romania.","DOI":"10.1109\/COMM48946.2020.9142021"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_40","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_41","first-page":"5512905","article-title":"Self-Supervised Learning With a Dual-Branch ResNet for Hyperspectral Image Classification","volume":"19","author":"Li","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1109\/LGRS.2017.2737823","article-title":"Recursive Autoencoders-Based Unsupervised Feature Learning for Hyperspectral Image Classification","volume":"14","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1109\/LGRS.2019.2921225","article-title":"Unsupervised Spectral\u2013Spatial Feature Extraction With Generalized Autoencoder for Hyperspectral Imagery","volume":"17","author":"Koda","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"4060","DOI":"10.1109\/JSTARS.2020.3008825","article-title":"Hyperspectral Classification Using Deep Belief Networks Based on Conjugate Gradient Update and Pixel-Centric Spectral Block Features","volume":"13","author":"Chen","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","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_47","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_48","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_49","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1109\/LGRS.2020.2991405","article-title":"Spectral\u2013Spatial Hyperspectral Image Classification Using Dual-Channel Capsule Networks","volume":"18","author":"Jiang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5522219","DOI":"10.1109\/TGRS.2023.3309245","article-title":"Fast Hyperspectral Image Classification Combining Transformers and SimAM-Based CNNs","volume":"61","author":"Liang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5518615","DOI":"10.1109\/TGRS.2021.3130716","article-title":"SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers","volume":"60","author":"Hong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5522214","DOI":"10.1109\/TGRS.2022.3221534","article-title":"Spectral\u2013Spatial Feature Tokenization Transformer for Hyperspectral Image Classification","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5536115","DOI":"10.1109\/TGRS.2022.3201145","article-title":"Local Semantic Feature Aggregation-Based Transformer for Hyperspectral Image Classification","volume":"60","author":"Tu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"10344","DOI":"10.1109\/JSTARS.2023.3328115","article-title":"A Dual Frequency Transformer Network for Hyperspectral Image Classification","volume":"16","author":"Qiao","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"6411","DOI":"10.1109\/JSTARS.2023.3288521","article-title":"Expansion Spectral\u2013Spatial Attention Network for Hyperspectral Image Classification","volume":"16","author":"Wang","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4400913","DOI":"10.1109\/TGRS.2024.3472066","article-title":"Masked Spectral\u2013Spatial Feature Prediction for Hyperspectral Image Classification","volume":"62","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","first-page":"5508718","article-title":"Spectral\u2013Spatial Masked Transformer with Supervised and Contrastive Learning for Hyperspectral Image Classification","volume":"61","author":"Huang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TGRS.2004.842481","article-title":"Investigation of the random forest framework for classification of hyperspectral data","volume":"43","author":"Ham","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","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 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref_60","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_61","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_62","doi-asserted-by":"crossref","first-page":"5539014","DOI":"10.1109\/TGRS.2022.3207933","article-title":"Hyperspectral Image Classification Using Group-Aware Hierarchical Transformer","volume":"60","author":"Mei","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"5511817","DOI":"10.1109\/TGRS.2024.3377610","article-title":"Hyperspectral Image Classification Using Groupwise Separable Convolutional Vision Transformer Network","volume":"62","author":"Zhao","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1560\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:34:55Z","timestamp":1760106895000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1560"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,27]]},"references-count":63,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16091560"],"URL":"https:\/\/doi.org\/10.3390\/rs16091560","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,4,27]]}}}