{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T21:13:00Z","timestamp":1775682780783,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T00:00:00Z","timestamp":1717545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Finance Science and Technology Project of Hainan Province","award":["ZDYF2021SHFZ063"],"award-info":[{"award-number":["ZDYF2021SHFZ063"]}]},{"name":"Finance Science and Technology Project of Hainan Province","award":["2018GNC110025"],"award-info":[{"award-number":["2018GNC110025"]}]},{"name":"Finance Science and Technology Project of Hainan Province","award":["ZR2020QF067"],"award-info":[{"award-number":["ZR2020QF067"]}]},{"name":"Finance Science and Technology Project of Hainan Province","award":["42301380"],"award-info":[{"award-number":["42301380"]}]},{"name":"Finance Science and Technology Project of Hainan Province","award":["42106179"],"award-info":[{"award-number":["42106179"]}]},{"name":"Finance Science and Technology Project of Hainan Province","award":["23-2-1-64-zyyd-jch"],"award-info":[{"award-number":["23-2-1-64-zyyd-jch"]}]},{"name":"Finance Science and Technology Project of Hainan Province","award":["2023KJ232"],"award-info":[{"award-number":["2023KJ232"]}]},{"name":"Shandong Key Research and Development Project","award":["ZDYF2021SHFZ063"],"award-info":[{"award-number":["ZDYF2021SHFZ063"]}]},{"name":"Shandong Key Research and Development Project","award":["2018GNC110025"],"award-info":[{"award-number":["2018GNC110025"]}]},{"name":"Shandong Key Research and Development Project","award":["ZR2020QF067"],"award-info":[{"award-number":["ZR2020QF067"]}]},{"name":"Shandong Key Research and Development Project","award":["42301380"],"award-info":[{"award-number":["42301380"]}]},{"name":"Shandong Key Research and Development Project","award":["42106179"],"award-info":[{"award-number":["42106179"]}]},{"name":"Shandong Key Research and Development Project","award":["23-2-1-64-zyyd-jch"],"award-info":[{"award-number":["23-2-1-64-zyyd-jch"]}]},{"name":"Shandong Key Research and Development Project","award":["2023KJ232"],"award-info":[{"award-number":["2023KJ232"]}]},{"name":"National Natural Science Foundation of China","award":["ZDYF2021SHFZ063"],"award-info":[{"award-number":["ZDYF2021SHFZ063"]}]},{"name":"National Natural Science Foundation of China","award":["2018GNC110025"],"award-info":[{"award-number":["2018GNC110025"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2020QF067"],"award-info":[{"award-number":["ZR2020QF067"]}]},{"name":"National Natural Science Foundation of China","award":["42301380"],"award-info":[{"award-number":["42301380"]}]},{"name":"National Natural Science Foundation of China","award":["42106179"],"award-info":[{"award-number":["42106179"]}]},{"name":"National Natural Science Foundation of China","award":["23-2-1-64-zyyd-jch"],"award-info":[{"award-number":["23-2-1-64-zyyd-jch"]}]},{"name":"National Natural Science Foundation of China","award":["2023KJ232"],"award-info":[{"award-number":["2023KJ232"]}]},{"name":"Qingdao Natural Science Foundation Grant","award":["ZDYF2021SHFZ063"],"award-info":[{"award-number":["ZDYF2021SHFZ063"]}]},{"name":"Qingdao Natural Science Foundation Grant","award":["2018GNC110025"],"award-info":[{"award-number":["2018GNC110025"]}]},{"name":"Qingdao Natural Science Foundation Grant","award":["ZR2020QF067"],"award-info":[{"award-number":["ZR2020QF067"]}]},{"name":"Qingdao Natural Science Foundation Grant","award":["42301380"],"award-info":[{"award-number":["42301380"]}]},{"name":"Qingdao Natural Science Foundation Grant","award":["42106179"],"award-info":[{"award-number":["42106179"]}]},{"name":"Qingdao Natural Science Foundation Grant","award":["23-2-1-64-zyyd-jch"],"award-info":[{"award-number":["23-2-1-64-zyyd-jch"]}]},{"name":"Qingdao Natural Science Foundation Grant","award":["2023KJ232"],"award-info":[{"award-number":["2023KJ232"]}]},{"name":"Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China","award":["ZDYF2021SHFZ063"],"award-info":[{"award-number":["ZDYF2021SHFZ063"]}]},{"name":"Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China","award":["2018GNC110025"],"award-info":[{"award-number":["2018GNC110025"]}]},{"name":"Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China","award":["ZR2020QF067"],"award-info":[{"award-number":["ZR2020QF067"]}]},{"name":"Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China","award":["42301380"],"award-info":[{"award-number":["42301380"]}]},{"name":"Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China","award":["42106179"],"award-info":[{"award-number":["42106179"]}]},{"name":"Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China","award":["23-2-1-64-zyyd-jch"],"award-info":[{"award-number":["23-2-1-64-zyyd-jch"]}]},{"name":"Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China","award":["2023KJ232"],"award-info":[{"award-number":["2023KJ232"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the continuous maturity of hyperspectral remote sensing imaging technology, it has been widely adopted by scholars to improve the performance of feature classification. However, due to the challenges in acquiring hyperspectral images and producing training samples, the limited training sample is a common problem that researchers often face. Furthermore, efficient algorithms are necessary to excavate the spatial and spectral information from these images, and then, make full use of this information with limited training samples. To solve this problem, a novel two-branch deep learning network model is proposed for extracting hyperspectral remote sensing features in this paper. In this model, one branch focuses on extracting spectral features using multi-scale convolution and a normalization-based attention module, while the other branch captures spatial features through small-scale dilation convolution and Euclidean Similarity Attention. Subsequently, pooling and layering techniques are employed to further extract abstract features after feature fusion. In the experiments conducted on two public datasets, namely, IP and UP, as well as our own labeled dataset, namely, YRE, the proposed DMAN achieves the best classification results, with overall accuracies of 96.74%, 97.4%, and 98.08%, respectively. Compared to the sub-optimal state-of-the-art methods, the overall accuracies are improved by 1.05, 0.42, and 0.51 percentage points, respectively. The advantage of this network structure is particularly evident in unbalanced sample environments. Additionally, we introduce a new strategy based on the RPNet, which utilizes a small number of principal components for feature classification after dimensionality reduction. The results demonstrate its effectiveness in uncovering compressed feature information, with an overall accuracy improvement of 0.68 percentage points. Consequently, our model helps mitigate the impact of data scarcity on model performance, thereby contributing positively to the advancement of hyperspectral remote sensing technology in practical applications.<\/jats:p>","DOI":"10.3390\/rs16112029","type":"journal-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T10:05:50Z","timestamp":1717581950000},"page":"2029","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A New Dual-Branch Embedded Multivariate Attention Network for Hyperspectral Remote Sensing Classification"],"prefix":"10.3390","volume":"16","author":[{"given":"Yuyi","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science & Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaopeng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2894-9627","authenticated-orcid":false,"given":"Jiahua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0133-8447","authenticated-orcid":false,"given":"Xiaodi","family":"Shang","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yabin","family":"Hu","sequence":"additional","affiliation":[{"name":"Lab of Marine Physics and Remote Sensing, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5972-5474","authenticated-orcid":false,"given":"Shichao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajie","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science & Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/JSTARS.2019.2892975","article-title":"A Parallel Gaussian\u2013Bernoulli Restricted Boltzmann Machine for Mining Area Classification with Hyperspectral Imagery","volume":"12","author":"Tan","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pascucci, S., Pignatti, S., Casa, R., Darvishzadeh, R., and Huang, W. (2020). Special Issue \u201cHyperspectral Remote Sensing of Agriculture and Vegetation\u201d. Remote Sens., 12.","DOI":"10.3390\/rs12213665"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kuras, A., Brell, M., Rizzi, J., and Burud, I. (2021). Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review. Remote Sens., 13.","DOI":"10.3390\/rs13173393"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/MGRS.2019.2912563","article-title":"Deep Learning for Classification of Hyperspectral Data: A Comparative Review","volume":"7","author":"Audebert","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, W., Chen, H., Liu, Q., Liu, H., Wang, Y., and Gui, G. (2022). Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification. Remote Sens., 14.","DOI":"10.3390\/rs14092215"},{"key":"ref_6","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_7","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_8","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1109\/TGRS.2020.2995709","article-title":"Local Constraint-Based Sparse Manifold Hypergraph Learning for Dimensionality Reduction of Hyperspectral Image","volume":"59","author":"Duan","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/LGRS.2019.2936652","article-title":"Sparse-Adaptive Hypergraph Discriminant Analysis for Hyperspectral Image Classification","volume":"17","author":"Luo","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4021","DOI":"10.1109\/TCYB.2020.2977461","article-title":"Local Manifold-Based Sparse Discriminant Learning for Feature Extraction of Hyperspectral Image","volume":"51","author":"Duan","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.neucom.2021.03.035","article-title":"A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples","volume":"448","author":"Jia","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/978-3-642-39712-7_18","article-title":"Evaluation of Hyperspectral Image Classification Using Random Forest and Fukunaga-Koontz Transform","volume":"Volume 7988","author":"Perner","year":"2013","journal-title":"Machine Learning and Data Mining in Pattern Recognition"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7140","DOI":"10.1109\/TGRS.2017.2743102","article-title":"PCA-Based Edge-Preserving Features for Hyperspectral Image Classification","volume":"55","author":"Kang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"183","DOI":"10.26599\/TST.2018.9010043","article-title":"Multiple Deep-Belief-Network-Based Spectral-Spatial Classification of Hyperspectral Images","volume":"24","author":"Mughees","year":"2019","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Mei, X., Pan, E., Ma, Y., Dai, X., Huang, J., Fan, F., Du, Q., Zheng, H., and Ma, J. (2019). Spectral-Spatial Attention Networks for Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11080963"},{"key":"ref_17","first-page":"5521018","article-title":"Multiscanning Strategy-Based Recurrent Neural Network for Hyperspectral Image Classification","volume":"60","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5401","DOI":"10.1109\/JSTARS.2022.3187009","article-title":"Multiscale DenseNet Meets with Bi-RNN for Hyperspectral Image Classification","volume":"15","author":"Liang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8657","DOI":"10.1109\/TGRS.2020.3037361","article-title":"CNN-Enhanced Graph Convolutional Network with Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification","volume":"59","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","article-title":"Graph Convolutional Networks for Hyperspectral Image Classification","volume":"59","author":"Hong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/JSTARS.2019.2900705","article-title":"CNN-Based Multilayer Spatial-Spectral Feature Fusion and Sample Augmentation with Local and Nonlocal Constraints for Hyperspectral Image Classification","volume":"12","author":"Feng","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TCYB.2018.2864670","article-title":"Feature Extraction for Classification of Hyperspectral and LiDAR Data Using Patch-to-Patch CNN","volume":"50","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TGRS.2018.2860125","article-title":"Deep Pyramidal Residual Networks for Spectral-Spatial Hyperspectral Image Classification","volume":"57","author":"Paoletti","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","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_26","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":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"10473","DOI":"10.1109\/TGRS.2020.3046840","article-title":"Spectral\u2013Spatial Fractal Residual Convolutional Neural Network with Data Balance Augmentation for Hyperspectral Classification","volume":"59","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5501505","DOI":"10.1109\/LGRS.2023.3241720","article-title":"2SRS: Two-Stream Residual Separable Convolution Neural Network for Hyperspectral Image Classification","volume":"20","author":"Zahisham","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"109020","DOI":"10.1016\/j.optlastec.2022.109020","article-title":"Superpixel-Guided Multifeature Tensor for Hyperspectral Image Classification with Limited Training Samples","volume":"159","author":"Wang","year":"2023","journal-title":"Opt. Laser Technol."},{"key":"ref_31","first-page":"5518416","article-title":"ELS2T: Efficient Lightweight Spectral-Spatial Transformer for Hyperspectral Image Classification","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lin, C., Wang, T., Dong, S., Zhang, Q., Yang, Z., and Gao, F. (2022). Hybrid Convolutional Network Combining 3D Depthwise Separable Convolution and Receptive Field Control for Hyperspectral Image Classification. Electronics, 11.","DOI":"10.3390\/electronics11233992"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5525715","DOI":"10.1109\/TGRS.2022.3162721","article-title":"ESSINet: Efficient Spatial\u2013Spectral Interaction Network for Hyperspectral Image Classification","volume":"60","author":"Lv","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5518817","DOI":"10.1109\/TGRS.2023.3301183","article-title":"Gabor-Modulated Grouped Separable Convolutional Network for Hyperspectral Image Classification","volume":"61","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liang, M., Wang, H., Yu, X., Meng, Z., Yi, J., and Jiao, L. (2022). Lightweight Multilevel Feature Fusion Network for Hyperspectral Image Classification. Remote Sens., 14.","DOI":"10.3390\/rs14010079"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, J., Huang, R., Guo, S., Li, L., Pei, Z., and Liu, B. (2022). HyperLiteNet: Extremely Lightweight Non-Deep Parallel Network for Hyperspectral Image Classification. Remote Sens., 14.","DOI":"10.3390\/rs14040866"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/JSTSP.2021.3063805","article-title":"Lightweight Tensor Attention-Driven ConvLSTM Neural Network for Hyperspectral Image Classification","volume":"15","author":"Hu","year":"2021","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_38","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_39","first-page":"5511817","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."},{"key":"ref_40","first-page":"5508518","article-title":"Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5511305","DOI":"10.1109\/LGRS.2021.3126125","article-title":"End-to-End Multilevel Hybrid Attention Framework for Hyperspectral Image Classification","volume":"19","author":"Xiang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Huang, W., Zhao, Z., Sun, L., and Ju, M. (2022). Dual-Branch Attention-Assisted CNN for Hyperspectral Image Classification. Remote Sens., 14.","DOI":"10.3390\/rs14236158"},{"key":"ref_43","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_44","first-page":"5501916","article-title":"Feedback Attention-Based Dense CNN for Hyperspectral Image Classification","volume":"60","author":"Yu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","first-page":"5518714","article-title":"Cross-Attention Spectral\u2013Spatial Network for Hyperspectral Image Classification","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","first-page":"5509816","article-title":"H2AN: Hierarchical Homogeneity-Attention Network for Hyperspectral Image Classification","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","first-page":"5535317","article-title":"Hyperspectral Image Classification Based on Multibranch Attention Transformer Networks","volume":"60","author":"Bai","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1109\/TCSVT.2022.3218284","article-title":"Exploring the Relationship Between Center and Neighborhoods: Central Vector Oriented Self-Similarity Network for Hyperspectral Image Classification","volume":"33","author":"Li","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.isprsjprs.2018.05.014","article-title":"Hyperspectral Image Classification via a Random Patches Network","volume":"142","author":"Xu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4753","DOI":"10.1109\/JSTARS.2021.3075771","article-title":"Hyperspectral Image Classification Via Spectral-Spatial Random Patches Network","volume":"14","author":"Cheng","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5503605","DOI":"10.1109\/LGRS.2021.3060876","article-title":"Multiscale Random Convolution Broad Learning System for Hyperspectral Image Classification","volume":"19","author":"Ma","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","unstructured":"Alkhatib, M.Q., Al-Saad, M., Aburaed, N., Almansoori, S., Zabalza, J., Marshall, S., and Al-Ahmad, H. (2023). Tri-CNN: A Three Branch Model for Hyperspectral Image Classification. Remote Sens., 15.","DOI":"10.3390\/rs15020316"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, E., Zhang, J., Bai, J., Bian, J., Fang, S., Zhan, T., and Feng, M. (2023). Attention-Embedded Triple-Fusion Branch CNN for Hyperspectral Image Classification. Remote Sens., 15.","DOI":"10.3390\/rs15082150"},{"key":"ref_55","unstructured":"Liu, Y., Shao, Z., Teng, Y., and Hoffmann, N. (2021). NAM: Normalization-based Attention Module. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/2029\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:54:15Z","timestamp":1760108055000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/2029"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,5]]},"references-count":55,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16112029"],"URL":"https:\/\/doi.org\/10.3390\/rs16112029","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,5]]}}}