{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:30:18Z","timestamp":1760149818588,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T00:00:00Z","timestamp":1695254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62101529"],"award-info":[{"award-number":["62101529"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Convolutional neural networks (CNNs) have shown outstanding feature extraction capability and become a hot topic in the field of hyperspectral image (HSI) classification. However, most of the prior works usually focus on designing deeper or wider network architectures to extract spatial and spectral features, which give rise to difficulty for optimization and more parameters along with higher computation. Moreover, how to learn spatial and spectral information more effectively is still being researched. To tackle the aforementioned problems, a decompressed spectral-spatial multiscale semantic feature network (DSMSFNet) for HSI classification is proposed. This model is composed of a decompressed spectral-spatial feature extraction module (DSFEM) and a multiscale semantic feature extraction module (MSFEM). The former is devised to extract more discriminative and representative global decompressed spectral-spatial features in a lightweight extraction manner, while the latter is constructed to expand the range of available receptive fields and generate clean multiscale semantic features at a granular level to further enhance the classification performance. Compared with progressive classification approaches, abundant experimental results on three benchmark datasets prove the superiority of our developed DSMSFNet model.<\/jats:p>","DOI":"10.3390\/rs15184642","type":"journal-article","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T21:16:49Z","timestamp":1695331009000},"page":"4642","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Decompressed Spectral-Spatial Multiscale Semantic Feature Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0691-4025","authenticated-orcid":false,"given":"Dongxu","family":"Liu","sequence":"first","affiliation":[{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5130-9049","authenticated-orcid":false,"given":"Qingqing","family":"Li","sequence":"additional","affiliation":[{"name":"China Automotive Engineering Research Institute Co., Ltd., Chongqing 410022, China"}]},{"given":"Meihui","family":"Li","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5284-2942","authenticated-orcid":false,"given":"Jianlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/79.974718","article-title":"Hyperspectral image data analysis","volume":"19","author":"Landgrebe","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4709","DOI":"10.1109\/TIP.2020.2968773","article-title":"Hyperspectral and multispectral image fusion using optimized twin dictionaries","volume":"29","author":"Han","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6547","DOI":"10.1109\/TGRS.2017.2729882","article-title":"Multiple Kernel learning for hyperspectral image classification: A review","volume":"55","author":"Gu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"61534","DOI":"10.1109\/ACCESS.2019.2916095","article-title":"Adaptive spatial\u2013spectral feature learning for hyperspectral image classification","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","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_6","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_7","doi-asserted-by":"crossref","first-page":"4340","DOI":"10.1109\/TGRS.2020.3016820","article-title":"More diverse means better: Multimodal deep learning meets remote sensing imagery classification","volume":"59","author":"Hong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3858","DOI":"10.1109\/TSP.2019.2922157","article-title":"Hyperspectral anomaly detection via global and local joint modeling of background","volume":"67","author":"Wu","year":"2019","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vaglio Laurin, G., Chan, J.C., Chen, Q., Lindsell, J.A., Coomes, D.A., Guerriero, L., Frate, F.D., Miglietta, F., and Valentini, R. (2014). Biodiversity Mapping in a Tropical West African Forest with Airborne Hyperspectral Data. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0097910"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"127167","DOI":"10.1109\/ACCESS.2020.3008029","article-title":"SSDANet: Spectral-spatial three-dimensional convolutional neural network for hyperspectral image classification","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Savelonas, M.A., Veinidis, C.V., and Bartsokas, T.K. (2022). Computer Vision and Pattern Recognition for the Analysis of 2D\/3D Remote Sensing Data in Geoscience: A Survey. Remote Sens., 14.","DOI":"10.3390\/rs14236017"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"7570","DOI":"10.1109\/JSTARS.2021.3099118","article-title":"Hyperspectral image classification using a hybrid 3D-2D convolutional neural networks","volume":"14","author":"Ghaderizadeh","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8693","DOI":"10.1109\/TGRS.2020.3047363","article-title":"Exploring the relationship between 2D\/3D convolution for hyperspectral image super-resolution","volume":"59","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1109\/LGRS.2012.2236818","article-title":"Learn multiple-kernel SVMs domain adaptation in hyperspectral data","volume":"10","author":"Sun","year":"2013","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"3973","DOI":"10.1109\/TGRS.2011.2129595","article-title":"Hyperspectral image classification using dictionary-based sparse representation","volume":"49","author":"Chen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TGRS.2011.2162649","article-title":"Spectral\u2013spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields","volume":"50","author":"Li","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","first-page":"5504205","article-title":"A Lightweight SpectralSpatial Convolution Module for Hyperspectral Image Classification","volume":"19","author":"Meng","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, C., and Zheng, Y. (2014, January 13\u201316). Hyperspectral remote sensing image classification based on combined SVM and LDA. Proceedings of the SPIE Asia Pacific Remote Sensing 2014, Beijing, China.","DOI":"10.1117\/12.2070688"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/LGRS.2011.2172185","article-title":"Linear versus nonlinear pca for the classification of hyperspectral data based on the extended morphological profiles","volume":"9","author":"Licciardi","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Villa, A., Chanussot, J., Jutten, C., Benediktsson, J., and Moussaoui, S. (2009, January 12\u201317). On the use of ICA for hyperspectral image analysis. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417363"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, S., Jia, X., and Zhang, B. (2013, January 21\u201326). Superpixel-based Markov random field for classification of hyperspectral images. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723581"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1109\/TGRS.2018.2861992","article-title":"Hyperspectral image classification in the presence of noisy labels","volume":"57","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chne","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","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. Sensors"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2016.2616355","article-title":"Hyperspectral Image Classification Using Deep Pixel-Pair Features","volume":"55","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhao, Y., Chan, J.C., and Yi, C. (2016, January 10\u201315). Hyperspectral image classification using two-channel deep convolutional neural network. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730324"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1109\/TGRS.2019.2952758","article-title":"Deep Feature Fusion via Two-Stream Convolutional Neural Network for Hyperspectral Image Classification","volume":"58","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TGRS.2020.2994057","article-title":"Residual Spectral-spatial Attention Network for Hyperspectral Image Classification","volume":"59","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","first-page":"5507714","article-title":"Spectral Partitioning Residual Network with Spatial Attention Mechanism for Hyperspectral Image Classification","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, W., Dou, S., Jiang, Z., and Sun, L. (2018). A fast dense spectral\u2013spatial convolution network framework for hyperspectral images classification. Remote Sens., 10.","DOI":"10.3390\/rs10071068"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"67","DOI":"10.3390\/rs9010067","article-title":"Spectral\u2013spatial classification of hyperspectral imagery with 3D convolutional neural network","volume":"9","author":"Li","year":"2017","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7790","DOI":"10.1109\/TGRS.2020.3038212","article-title":"Attention-Aware Pseudo-3-D Convolutional Neural Network for Hyperspectral Image Classification","volume":"59","author":"Lin","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhao, S., Li, W., Du, Q., and Ran, Q. (2018, January 22\u201327). Hyperspectral classification based on Siamese neural network using spectral\u2013spatial feature. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519286"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, M., Li, B., and Chen, H. (2017, January 17\u201320). Multi-scale 3D deep convolutional neural network for hyperspectral image classification. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8297014"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2563","DOI":"10.1109\/JSTARS.2021.3056124","article-title":"Densely Connected Multiscale Attention Network for Hyperspectral Image Classification","volume":"14","author":"Gao","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Xue, Y., Zeng, D., Chen, F., Wang, Y., and Zhang, Z. (2020). A new dataset and deep residual spectral spatial network for hyperspectral image classification. Symmetry, 12.","DOI":"10.3390\/sym12040561"},{"key":"ref_41","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_42","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_43","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.3390\/rs10091454","article-title":"Deep & dense convolutional neural network for hyperspectral image classification","volume":"10","author":"Paoletti","year":"2018","journal-title":"Remote Sens."},{"key":"ref_44","first-page":"5503305","article-title":"Hyperspectral Image Classification With Multiattention Fusion Network","volume":"19","author":"Li","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3396","DOI":"10.1109\/TGRS.2020.3008286","article-title":"Multiscale Residual Network with Mixed Depthwise Convolution for Hyperspectral Image Classification","volume":"59","author":"Gao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., and Liu, W. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_50","unstructured":"Jie, H., Li, S., and Gang, S. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"8180","DOI":"10.1109\/JSTARS.2021.3103176","article-title":"A Multiscale Dual-Branch Feature Fusion and Attention Network for Hyperspectral Images Classification","volume":"14","author":"Gao","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Xu, Q., Xiao, Y., Wang, D., and Luo, B. (2020). CSA-MSO3DCNN: Multiscale Octave 3D CNN with Channel and Spatial Attention for Hyperspectral Image Classification. Remote Sens., 12.","DOI":"10.3390\/rs12010188"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1109\/JSTARS.2016.2531747","article-title":"Hyperspectral airborne \u201cViareggio 2013 Trial\u201d data collection for detection algorithm assessment","volume":"9","author":"Acito","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","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_55","unstructured":"Zhang, X., Wang, T., and Yang, Y. (2020). Hyperspectral image classification based on multi-scale residual network with attention mechanism. arXiv."},{"key":"ref_56","unstructured":"Ahmad, M., Shabbir, S., Raza, R.A., Mazzara, M., Distefano, S., and Khan, A.M. (2021, January 20\u201325). Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1109\/JSTARS.2022.3145917","article-title":"Multiscale Densely Connected Attention Network for Hyperspectral Image Classification","volume":"15","author":"Wang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4642\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:55:22Z","timestamp":1760129722000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4642"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,21]]},"references-count":58,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15184642"],"URL":"https:\/\/doi.org\/10.3390\/rs15184642","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,9,21]]}}}