{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:22:39Z","timestamp":1780053759676,"version":"3.54.0"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"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":["42050103"],"award-info":[{"award-number":["42050103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2018YFB0505002"],"award-info":[{"award-number":["2018YFB0505002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["42050103"],"award-info":[{"award-number":["42050103"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2018YFB0505002"],"award-info":[{"award-number":["2018YFB0505002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral images (HSI) contain powerful spectral characterization capabilities and are widely used especially for classification applications. However, the rich spectrum contained in HSI also increases the difficulty of extracting useful information, which makes the feature extraction method significant as it enables effective expression and utilization of the spectrum. Traditional HSI feature extraction methods design spectral features manually, which is likely to be limited by the complex spectral information within HSI. Recently, data-driven methods, especially the use of convolutional neural networks (CNNs), have shown great improvements in performance when processing image data owing to their powerful automatic feature learning and extraction abilities and are also widely used for HSI feature extraction and classification. The CNN extracts features based on the convolution operation. Nevertheless, the local perception of the convolution operation makes CNN focus on the local spectral features (LSF) and weakens the description of features between long-distance spectral ranges, which will be referred to as global spectral features (GSF) in this study. LSF and GSF describe the spectral features from two different perspectives and are both essential for determining the spectrum. Thus, in this study, a local-global spectral feature (LGSF) extraction and optimization method is proposed to jointly consider the LSF and GSF for HSI classification. To increase the relationship between spectra and the possibility to obtain features with more forms, we first transformed the 1D spectral vector into a 2D spectral image. Based on the spectral image, the local spectral feature extraction module (LSFEM) and the global spectral feature extraction module (GSFEM) are proposed to automatically extract the LGSF. The loss function for spectral feature optimization is proposed to optimize the LGSF and obtain improved class separability inspired by contrastive learning. We further enhanced the LGSF by introducing spatial relation and designed a CNN constructed using dilated convolution for classification. The proposed method was evaluated on four widely used HSI datasets, and the results highlighted its comprehensive utilization of spectral information as well as its effectiveness in HSI classification.<\/jats:p>","DOI":"10.3390\/rs15071803","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T07:05:25Z","timestamp":1679987125000},"page":"1803","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Local and Global Spectral Features for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2149-9109","authenticated-orcid":false,"given":"Zeyu","family":"Xu","sequence":"first","affiliation":[{"name":"School of Earth Science, Zhejiang University, Hangzhou 310030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3540-0309","authenticated-orcid":false,"given":"Cheng","family":"Su","sequence":"additional","affiliation":[{"name":"School of Earth Science, Zhejiang University, Hangzhou 310030, China"},{"name":"Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou 310030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1355-2854","authenticated-orcid":false,"given":"Shirou","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Earth Science, Zhejiang University, Hangzhou 310030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaocan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Earth Science, Zhejiang University, Hangzhou 310030, China"},{"name":"Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou 310030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"ref_1","first-page":"5501916","article-title":"Feedback Attention-Based Dense CNN for Hyperspectral Image Classification","volume":"60","author":"Yu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3791","DOI":"10.1109\/TGRS.2019.2957251","article-title":"Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification","volume":"58","author":"Hong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1016\/j.cageo.2007.11.001","article-title":"Detection of pre-defined boundaries between hydrothermal alteration zones using rotation-variant template matching","volume":"34","author":"Hein","year":"2008","journal-title":"Comput. Geosci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.cageo.2013.01.018","article-title":"Terrestrial lidar and hyperspectral data fusion products for geological outcrop analysis","volume":"54","author":"Buckley","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/S0168-1699(03)00020-6","article-title":"Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn","volume":"39","author":"Goel","year":"2003","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4117","DOI":"10.1109\/JSTARS.2016.2577339","article-title":"Crop Classification Based on Feature Band Set Construction and Object-Oriented Approach Using Hyperspectral Images","volume":"9","author":"Zhang","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral Remote Sensing Data Analysis and Future Challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TGRS.2004.842478","article-title":"Classification of hyperspectral data from urban areas based on extended morphological profiles","volume":"43","author":"Benediktsson","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1080\/01431161.2016.1253899","article-title":"Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hyperspectral data","volume":"38","author":"Liu","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5474","DOI":"10.1080\/01431161.2021.1918792","article-title":"A hyperspectral method of inverting copper signals in mineral deposits based on an improved gradient-boosting regression tree","volume":"42","author":"Xie","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TGRS.2018.2860125","article-title":"Deep Pyramidal Residual Networks for Spectral\u2013Spatial Hyperspectral Image Classification","volume":"57","author":"Paoletti","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1109\/TGRS.2017.2762593","article-title":"Multifeature Hyperspectral Image Classification with Local and Nonlocal Spatial Information via Markov Random Field in Semantic Space","volume":"56","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1109\/TGRS.2019.2940991","article-title":"Supervised Functional Data Discriminant Analysis for Hyperspectral Image Classification","volume":"58","author":"Ye","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3090410","article-title":"Local Similarity-Based Spatial\u2013Spectral Fusion Hyperspectral Image Classification with Deep CNN and Gabor Filtering","volume":"60","author":"Bhatti","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1109\/36.295042","article-title":"Efficient maximum likelihood classification for imaging spectrometer data sets","volume":"32","author":"Jia","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/LGRS.2003.822879","article-title":"Visual Method for Spectral Band Selection","volume":"1","author":"Ifarraguerri","year":"2004","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/TGRS.2005.863297","article-title":"Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis","volume":"44","author":"Wang","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1109\/36.803411","article-title":"A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification","volume":"37","author":"Chang","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1080\/01431160701802471","article-title":"Phase correlation based redundancy removal in feature weighting band selection for hyperspectral images","volume":"29","author":"Demir","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3625","DOI":"10.1080\/01431160802592518","article-title":"Combining magnitude and shape features for hyperspectral classification","volume":"30","author":"Chen","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1109\/TGRS.2018.2860464","article-title":"Feature Extraction with Multiscale Covariance Maps for Hyperspectral Image Classification","volume":"57","author":"He","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4581","DOI":"10.1109\/TGRS.2018.2828029","article-title":"SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery","volume":"56","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","first-page":"1","article-title":"Fusion of PCA and Segmented-PCA Domain Multiscale 2-D-SSA for Effective Spectral-Spatial Feature Extraction and Data Classification in Hyperspectral Imagery","volume":"60","author":"Fu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","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_26","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_27","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1109\/LGRS.2020.2966987","article-title":"A Multiscale Deep Learning Approach for High-Resolution Hyperspectral Image Classification","volume":"18","author":"Safari","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"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":"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_30","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.1109\/TGRS.2018.2794326","article-title":"Hyperspectral Image Classification with Deep Feature Fusion Network","volume":"56","author":"Song","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4420","DOI":"10.1109\/TGRS.2018.2818945","article-title":"3-D Deep Learning Approach for Remote Sensing Image Classification","volume":"56","author":"Hamida","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral\u2014Spatial 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_35","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wei, F., Feng, F., and Wang, C. (2020). Spatial\u2013Spectral Feature Refinement for Hyperspectral Image Classification Based on Attention-Dense 3D-2D-CNN. Sensors, 20.","DOI":"10.3390\/s20185191"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yuan, S., Song, G., Huang, G., and Wang, Q. (2022). Reshaping Hyperspectral Data into a Two-Dimensional Image for a CNN Model to Classify Plant Species from Reflectance. Remote Sens., 14.","DOI":"10.3390\/rs14163972"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, F., and Liu, H.P. (2021, January 20\u201325). Understanding the Behaviour of Contrastive Loss. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00252"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). PyTorch: An imperative style, high-performance deep learning library. Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A Library for Support Vector Machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_41","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/7\/1803\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:05:04Z","timestamp":1760123104000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/7\/1803"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,28]]},"references-count":41,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15071803"],"URL":"https:\/\/doi.org\/10.3390\/rs15071803","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,28]]}}}