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Council","doi-asserted-by":"publisher","award":["202206420044"],"award-info":[{"award-number":["202206420044"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Convolutional neural networks (CNNs) have recently been demonstrated to be able to substantially improve the land cover classification accuracy of hyperspectral images. Meanwhile, the rapidly developing capacity for satellite and airborne image spectroscopy as well as the enormous archives of spectral data have imposed increasing demands on the computational efficiency of CNNs. Here, we propose a novel CNN framework that integrates one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) CNNs to obtain highly accurate and fast land cover classification from airborne hyperspectral images. To achieve this, we first used 3D CNNs to derive both spatial and spectral features from hyperspectral images. Then, we successively utilized a 2D CNN and a 1D CNN to efficiently acquire higher-level representations of spatial or spectral features. Finally, we leveraged the information obtained from the aforementioned steps for land cover classification. We assessed the performance of the proposed method using two openly available datasets (the Indian Pines dataset and the Wuhan University dataset). Our results showed that the overall classification accuracy of the proposed method in the Indian Pines and Wuhan University datasets was 99.65% and 99.85%, respectively. Compared to the state-of-the-art 3D CNN model and HybridSN model, the training times for our model in the two datasets were reduced by an average of 60% and 40%, respectively, while maintaining comparable classification accuracy. Our study demonstrates that the integration of 1D, 2D, and 3D CNNs effectively improves the computational efficiency of land cover classification with hyperspectral images while maintaining high accuracy. Our innovation offers significant advantages in terms of efficiency and robustness for the processing of large-scale hyperspectral images.<\/jats:p>","DOI":"10.3390\/rs15194797","type":"journal-article","created":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T04:28:08Z","timestamp":1696220888000},"page":"4797","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Integrated 1D, 2D, and 3D CNNs Enable Robust and Efficient Land Cover Classification from Hyperspectral Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9613-7762","authenticated-orcid":false,"given":"Jinxiang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1138-8464","authenticated-orcid":false,"given":"Tiejun","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7446-8429","authenticated-orcid":false,"given":"Andrew","family":"Skidmore","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, The Netherlands"},{"name":"Department of Environmental Science, Macquarie University, Sydney, NSW 2109, Australia"}]},{"given":"Yaqin","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0110-3637","authenticated-orcid":false,"given":"Peng","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China"},{"name":"Hubei Luojia Laboratory, Wuhan 430079, China"},{"name":"International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan 430072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9376-1148","authenticated-orcid":false,"given":"Kefei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Satellite Positioning for Atmosphere, Climate and Environment Research Center, School of Science, Royal Melbourne Institute of Technology (RMIT University), Melbourne, VIC 3000, Australia"},{"name":"Bei-Stars Geospatial Information Innovation Institute, Nanjing 210000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"ref_1","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. 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