{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T11:32:21Z","timestamp":1763724741379,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"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":["U21A2013","PBD2023-28","SKLAO2021001A01","HBHY2302","21373301D","WL2023007","OFSLRSS202312","2642022009","GASI-01-DLYG-WIND0","202401001","2023(A)003"],"award-info":[{"award-number":["U21A2013","PBD2023-28","SKLAO2021001A01","HBHY2302","21373301D","WL2023007","OFSLRSS202312","2642022009","GASI-01-DLYG-WIND0","202401001","2023(A)003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation of State Key Laboratory of Public Big Data","award":["U21A2013","PBD2023-28","SKLAO2021001A01","HBHY2302","21373301D","WL2023007","OFSLRSS202312","2642022009","GASI-01-DLYG-WIND0","202401001","2023(A)003"],"award-info":[{"award-number":["U21A2013","PBD2023-28","SKLAO2021001A01","HBHY2302","21373301D","WL2023007","OFSLRSS202312","2642022009","GASI-01-DLYG-WIND0","202401001","2023(A)003"]}]},{"name":"State Key Laboratory of applied optics","award":["U21A2013","PBD2023-28","SKLAO2021001A01","HBHY2302","21373301D","WL2023007","OFSLRSS202312","2642022009","GASI-01-DLYG-WIND0","202401001","2023(A)003"],"award-info":[{"award-number":["U21A2013","PBD2023-28","SKLAO2021001A01","HBHY2302","21373301D","WL2023007","OFSLRSS202312","2642022009","GASI-01-DLYG-WIND0","202401001","2023(A)003"]}]},{"name":"Hebei Key Laboratory of Ocean Dynamics, Resources and Environments","award":["U21A2013","PBD2023-28","SKLAO2021001A01","HBHY2302","21373301D","WL2023007","OFSLRSS202312","2642022009","GASI-01-DLYG-WIND0","202401001","2023(A)003"],"award-info":[{"award-number":["U21A2013","PBD2023-28","SKLAO2021001A01","HBHY2302","21373301D","WL2023007","OFSLRSS202312","2642022009","GASI-01-DLYG-WIND0","202401001","2023(A)003"]}]},{"name":"S &amp; 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Recently, graph convolutional networks (GCNs) have been proposed to process graph-structured data in non-Euclidean domains, and have been used for HSIC. The superpixel segmentation should be implemented first in the GCN-based methods, however, it is difficult to manually select the optimal superpixel segmentation sizes to obtain the useful information for classification. To solve this problem, we constructed a HSIC model based on a multiscale feature search-based graph convolutional network (MFSGCN) in this study. Firstly, pixel-level features of HSIs are extracted sequentially using 3D asymmetric decomposition convolution and 2D convolution. Then, superpixel-level features at different scales are extracted using multilayer GCNs. Finally, the neural architecture search (NAS) method is used to automatically assign different weights to different scales of superpixel features. Thus, a more discriminative feature map is obtained for classification. Compared with other GCN-based networks, the MFSGCN network can automatically capture features and obtain higher classification accuracy. The proposed MFSGCN model was implemented on three commonly used HSI datasets and compared to some state-of-the-art methods. The results confirm that MFSGCN effectively improves accuracy.<\/jats:p>","DOI":"10.3390\/rs16132328","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T05:03:07Z","timestamp":1719378187000},"page":"2328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Multiscale Feature Search-Based Graph Convolutional Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9692-4221","authenticated-orcid":false,"given":"Ke","family":"Wu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"},{"name":"State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"School of Geophysics and Geomatics, China University of Geoscience, Wuhan 430074, China"}]},{"given":"Yanting","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geoscience, Wuhan 430074, China"}]},{"given":"Ying","family":"An","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"Hebei Key Laboratory of Ocean Dynamics, Resources and Environments, Qinhuangdao 066004, China"}]},{"given":"Suyi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geoscience, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,26]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Multilevel Superpixel Structured Graph U-Nets for Hyperspectral Image Classification","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., and Shang, J. (2020). Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1080\/01431161.2012.727039","article-title":"Close-Range Hyperspectral Imaging for Geological Field Studies: Workflow and Methods","volume":"34","author":"Kurz","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/2150704X.2019.1670518","article-title":"Brightness Gradient-Corrected Hyperspectral Image Mosaics for Fractional Vegetation Cover Mapping in Northern California","volume":"11","author":"Okujeni","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"44247","DOI":"10.1109\/ACCESS.2019.2908991","article-title":"NAS-Unet: Neural Architecture Search for Medical Image Segmentation","volume":"7","author":"Weng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","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":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3813","DOI":"10.1109\/TGRS.2018.2888485","article-title":"Semisupervised Stacked Autoencoder With Cotraining for Hyperspectral Image Classification","volume":"57","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3516","DOI":"10.1109\/TGRS.2017.2675902","article-title":"Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification","volume":"55","author":"Zhong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5384","DOI":"10.1109\/TGRS.2019.2899129","article-title":"Cascaded Recurrent Neural Networks for Hyperspectral Image Classification","volume":"57","author":"Hang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"5502012","DOI":"10.1109\/TGRS.2021.3058321","article-title":"Accelerating Convolutional Neural Network-Based Hyperspectral Image Classification by Step Activation Quantization","volume":"60","author":"Mei","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5046","DOI":"10.1109\/TGRS.2018.2805286","article-title":"Generative Adversarial Networks for Hyperspectral Image Classification","volume":"56","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.1109\/TGRS.2019.2949180","article-title":"Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification","volume":"58","author":"Wan","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6010305","DOI":"10.1109\/LGRS.2022.3178708","article-title":"Unifying Label Propagation and Graph Sparsification for Hyperspectral Image Classification","volume":"19","author":"Hu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","first-page":"5526116","article-title":"EMS-GCN: An End-to-End Mixhop Superpixel-Based Graph Convolutional Network for Hyperspectral Image Classification","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4561","DOI":"10.1109\/JSTARS.2021.3074469","article-title":"Multiscale Graph Sample and Aggregate Network With Context-Aware Learning for Hyperspectral Image Classification","volume":"14","author":"Ding","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5504918","DOI":"10.1109\/TGRS.2023.3253248","article-title":"DSR-GCN: Differentiated-Scale Restricted Graph Convolutional Network for Few-Shot Hyperspectral Image Classification","volume":"61","author":"Xue","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5506514","DOI":"10.1109\/TGRS.2023.3304311","article-title":"Two-Branch Deeper Graph Convolutional Network for Hyperspectral Image Classification","volume":"61","author":"Yu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","first-page":"55","article-title":"Neural Architecture Search: A Survey","volume":"20","author":"Elsken","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8754","DOI":"10.1109\/TGRS.2021.3049377","article-title":"NAS-Guided Lightweight Multiscale Attention Fusion Network for Hyperspectral Image Classification","volume":"59","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhang, C., Cai, Z., Yang, J., Zhou, Z., and Gong, X. (2021). Continuous Particle Swarm Optimization-Based Deep Learning Architecture Search for Hyperspectral Image Classification. Remote Sens., 13.","DOI":"10.3390\/rs13061082"},{"key":"ref_24","first-page":"5508519","article-title":"3-D-ANAS: 3-D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5531116","DOI":"10.1109\/TGRS.2022.3180685","article-title":"Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image Classification","volume":"60","author":"Xue","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7048","DOI":"10.1109\/TGRS.2019.2910603","article-title":"Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification","volume":"57","author":"Chen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5536216","DOI":"10.1109\/TGRS.2022.3198931","article-title":"Superpixel Spectral\u2013Spatial Feature Fusion Graph Convolution Network for Hyperspectral Image Classification","volume":"60","author":"Gong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liu, S., Zhang, Y., and Chen, W. (2022). RS-DARTS: A Convolutional Neural Architecture Search for Remote Sensing Image Scene Classification. Remote Sens., 14.","DOI":"10.3390\/rs14010141"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, L., Sun, J., Sun, L., Kobashi, H., and Imamura, N. (2021, January 10\u201315). NAS-EOD: An End-to-End Neural Architecture Search Method for Efficient Object Detection. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413209"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mo\u017cejko, M., Latkowski, T., Treszczotko, \u0141., Szafraniuk, M., and Trojanowski, K. (2020, January 14\u201319). Superkernel Neural Architecture Search for Image Denoising. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00250"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3473330","article-title":"Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap","volume":"54","author":"Xie","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_32","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_33","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_34","doi-asserted-by":"crossref","first-page":"7831","DOI":"10.1109\/TGRS.2020.3043267","article-title":"Attention-Based Adaptive Spectral\u2013Spatial Kernel ResNet for Hyperspectral Image Classification","volume":"59","author":"Roy","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2328\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:04:35Z","timestamp":1760108675000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2328"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,26]]},"references-count":34,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16132328"],"URL":"https:\/\/doi.org\/10.3390\/rs16132328","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,6,26]]}}}