{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T02:57:24Z","timestamp":1774493844240,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":["62171247"],"award-info":[{"award-number":["62171247"]}],"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":["41921781"],"award-info":[{"award-number":["41921781"]}],"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":["ZR2016FQ14"],"award-info":[{"award-number":["ZR2016FQ14"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Foundation of Shandong Province","award":["62171247"],"award-info":[{"award-number":["62171247"]}]},{"name":"Natural Foundation of Shandong Province","award":["41921781"],"award-info":[{"award-number":["41921781"]}]},{"name":"Natural Foundation of Shandong Province","award":["ZR2016FQ14"],"award-info":[{"award-number":["ZR2016FQ14"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Since Hyperspectral Images (HSIs) contain plenty of ground object information, they are widely used in fine-grain classification of ground objects. However, some ground objects are similar and the number of spectral bands is far higher than the number of the ground object categories. Therefore, it is hard to deeply explore the spatial\u2013spectral joint features with greater discrimination. To mine the spatial\u2013spectral features of HSIs, a Shallow-to-Deep Feature Enhancement (SDFE) model with three modules based on Convolutional Neural Networks (CNNs) and Vision-Transformer (ViT) is proposed. Firstly, the bands containing important spectral information are selected using Principal Component Analysis (PCA). Secondly, a two-layer 3D-CNN-based Shallow Spatial\u2013Spectral Feature Extraction (SSSFE) module is constructed to preserve the spatial and spectral correlations across spaces and bands at the same time. Thirdly, to enhance the nonlinear representation ability of the network and avoid the loss of spectral information, a channel attention residual module based on 2D-CNN is designed to capture the deeper spatial\u2013spectral complementary information. Finally, a ViT-based module is used to extract the joint spatial\u2013spectral features (SSFs) with greater robustness. Experiments are carried out on Indian Pines (IP), Pavia University (PU) and Salinas (SA) datasets. The experimental results show that better classification results can be achieved by using the proposed feature enhancement method as compared to other methods.<\/jats:p>","DOI":"10.3390\/rs15010261","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T03:00:59Z","timestamp":1672628459000},"page":"261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Shallow-to-Deep Spatial\u2013Spectral Feature Enhancement for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6975-1732","authenticated-orcid":false,"given":"Lijian","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyu","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiliang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyuan","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6843-6722","authenticated-orcid":false,"given":"Yuanxin","family":"Ye","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9949-4693","authenticated-orcid":false,"given":"Kun","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, L., Tong, Q., and Sun, X. 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