{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:49:19Z","timestamp":1760150959530,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T00:00:00Z","timestamp":1643587200000},"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":["62002083","61971153"],"award-info":[{"award-number":["62002083","61971153"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Heilongjiang Provincial Natural Science Foundation of China","award":["LH2021F012"],"award-info":[{"award-number":["LH2021F012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image classification (HSIC) methods usually require more training samples for better classification performance. However, a large number of labeled samples are difficult to obtain because it is cost- and time-consuming to label an HSI in a pixel-wise way. Therefore, how to overcome the problem of insufficient accuracy and stability under the condition of small labeled training sample size (SLTSS) is still a challenge for HSIC. In this paper, we proposed a novel multiple superpixel graphs learning method based on adaptive multiscale segmentation (MSGLAMS) for HSI classification to address this problem. First, the multiscale-superpixel-based framework can reduce the adverse effect of improper selection of a superpixel segmentation scale on the classification accuracy while saving the cost to manually seek a suitable segmentation scale. To make full use of the superpixel-level spatial information of different segmentation scales, a novel two-steps multiscale selection strategy is designed to adaptively select a group of complementary scales (multiscale). To fix the bias and instability of a single model, multiple superpixel-based graphical models obatined by constructing superpixel contracted graph of fusion scales are developed to jointly predict the final results via a pixel-level fusion strategy. Experimental results show that the proposed MSGLAMS has better performance when compared with other state-of-the-art algorithms. Specifically, its overall accuracy achieves 94.312%, 99.217%, 98.373% and 92.693% on Indian Pines, Salinas and University of Pavia, and the more challenging dataset Houston2013, respectively.<\/jats:p>","DOI":"10.3390\/rs14030681","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T22:16:18Z","timestamp":1643753778000},"page":"681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Multiple Superpixel Graphs Learning Based on Adaptive Multiscale Segmentation for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Chunhui","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Boao","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Shou","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Wenxiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1109\/JSTARS.2013.2295313","article-title":"Gabor-Filtering-Based Nearest Regularized Subspace for Hyperspectral Image Classification","volume":"7","author":"Li","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Jafarzadeh, H., Mahdianpari, M., Gill, E., Mohammadimanesh, F., and Homayouni, S. (2021). Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation. Remote Sens., 13.","key":"ref_2","DOI":"10.3390\/rs13214405"},{"doi-asserted-by":"crossref","unstructured":"Liu, Q., Wu, Z., Jia, X., Xu, Y., and Wei, Z. (2021). From Local to Global: Class Feature Fused Fully Convolutional Network for Hyperspectral Image Classification. Remote Sens., 13.","key":"ref_3","DOI":"10.3390\/rs13245043"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1762","DOI":"10.1109\/LGRS.2019.2953525","article-title":"Progressive Band Selection Processing of Hyperspectral Image Classification","volume":"17","author":"Song","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5093","DOI":"10.1109\/TGRS.2017.2702197","article-title":"A Subpixel Target Detection Approach to Hyperspectral Image Classification","volume":"55","author":"Xue","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1109\/JSTARS.2013.2295513","article-title":"Ensemble Learning in Hyperspectral Image Classification: Toward Selecting a Favorable Bias-Variance Tradeoff","volume":"7","author":"Merentitis","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2004.07.004","article-title":"Evaluation of hyperspectral remote sensing as a means of environmental monitoring in the St. Austell China clay (kaolin) region, Cornwall, UK","volume":"93","author":"Ellis","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111338","DOI":"10.1016\/j.rse.2019.111338","article-title":"Remote sensing monitoring of multi-scale watersheds impermeability for urban hydrological evaluation","volume":"232","author":"Shao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.isprsjprs.2014.09.016","article-title":"Evaluating the performance of a new classifier\u2014The GP-OAD: A comparison with existing methods for classifying rock type and mineralogy from hyperspectral imagery","volume":"98","author":"Schneider","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106237","DOI":"10.1016\/j.compag.2021.106237","article-title":"3D global mapping of large-scale unstructured orchard integrating eye-in-hand stereo vision and SLAM","volume":"187","author":"Chen","year":"2021","journal-title":"Comput. Electron. Agric."},{"doi-asserted-by":"crossref","unstructured":"Cao, X., Yan, H., Huang, Z., Ai, S., Xu, Y., Fu, R., and Zou, X. (2021). A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator. Agronomy, 11.","key":"ref_12","DOI":"10.3390\/agronomy11112286"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"705021","DOI":"10.3389\/fpls.2021.705021","article-title":"Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point","volume":"12","author":"Wu","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MGRS.2020.2979764","article-title":"Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox","volume":"8","author":"Rasti","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7758","DOI":"10.1109\/TGRS.2020.3034133","article-title":"Deep Multiview Learning for Hyperspectral Image Classification","volume":"59","author":"Liu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1866","DOI":"10.1109\/JSTARS.2019.2911987","article-title":"Hyperspectral Image Classification Method Based on CNN Architecture Embedding with Hashing Semantic Feature","volume":"12","author":"Yu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Liu, D., Han, G., Liu, P., Yang, H., Sun, X., Li, Q., and Wu, J. (2021). A Novel 2D-3D CNN with Spectral-Spatial Multi-Scale Feature Fusion for Hyperspectral Image Classification. Remote Sens., 13.","key":"ref_17","DOI":"10.3390\/rs13224621"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/MGRS.2019.2912563","article-title":"Deep Learning for Classification of Hyperspectral Data: A Comparative Review","volume":"7","author":"Audebert","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_19","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":"Benoit","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","first-page":"1","article-title":"Hyperspectral Image Classification Based on Kernel-Guided Deformable Convolution and Double-Window Joint Bilateral Filter","volume":"19","author":"Zhao","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6594","DOI":"10.1109\/TGRS.2017.2730583","article-title":"A Batch-Mode Regularized Multimetric Active Learning Framework for Classification of Hyperspectral Images","volume":"55","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1109\/LGRS.2017.2787338","article-title":"Hyperspectral Image Classification via Fusing Correlation Coefficient and Joint Sparse Representation","volume":"15","author":"Tu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4263","DOI":"10.1109\/TGRS.2019.2962014","article-title":"Multilabel Sample Augmentation-Based Hyperspectral Image Classification","volume":"58","author":"Hao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","first-page":"1","article-title":"Polygon Structure-Guided Hyperspectral Image Classification with Single Sample for Strong Geometric Characteristics Scenes","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1109\/LGRS.2013.2292892","article-title":"Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples","volume":"11","author":"Imani","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Chen, Y. (2021). Multiscale Weighted Adjacent Superpixel-Based Composite Kernel for Hyperspectral Image Classification. Remote Sens., 13.","key":"ref_26","DOI":"10.3390\/rs13040820"},{"doi-asserted-by":"crossref","unstructured":"Zhao, Y., and Yan, F. (2021). Hyperspectral Image Classification Based on Sparse Superpixel Graph. Remote Sens., 13.","key":"ref_27","DOI":"10.3390\/rs13183592"},{"key":"ref_28","first-page":"1","article-title":"Multiscale-Superpixel-Based SparseCEM for Hyperspectral Target Detection","volume":"19","author":"Yang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1109\/LGRS.2017.2755061","article-title":"Multiscale Superpixel-Level Subspace-Based Support Vector Machines for Hyperspectral Image Classification","volume":"14","author":"Yu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1109\/TMM.2019.2928491","article-title":"Multiscale Superpixel-Based Hyperspectral Image Classification Using Recurrent Neural Networks With Stacked Autoencoders","volume":"22","author":"Shi","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7230","DOI":"10.1109\/TGRS.2018.2849443","article-title":"Superpixel-Based Unsupervised Band Selection for Classification of Hyperspectral Images","volume":"56","author":"Yang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1109\/LGRS.2019.2945546","article-title":"Semisupervised Classification Based on SLIC Segmentation for Hyperspectral Image","volume":"17","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Liu, M.Y., Tuzel, O., Ramalingam, S., and Chellappa, R. (2011, January 20\u201325). Entropy rate superpixel segmentation. Proceedings of the CVPR 2011, Colorado Springs, CO, USA.","key":"ref_34","DOI":"10.1109\/CVPR.2011.5995323"},{"doi-asserted-by":"crossref","unstructured":"He, Z., Shen, Y., Zhang, M., Wang, Q., Wang, Y., and Yu, R. (2014, January 12\u201315). Spectral-spatial hyperspectral image classification via SVM and superpixel segmentation. Proceedings of the 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, Montevideo, Uruguay.","key":"ref_35","DOI":"10.1109\/I2MTC.2014.6860780"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7307","DOI":"10.1109\/TGRS.2019.2912330","article-title":"Hyperspectral Image Classification with Small Training Sample Size Using Superpixel-Guided Training Sample Enlargement","volume":"57","author":"Zheng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","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":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4180","DOI":"10.1109\/TGRS.2019.2961599","article-title":"Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification","volume":"58","author":"Sellars","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1109\/TGRS.2020.2994260","article-title":"Superpixel-Based Reweighted Low-Rank and Total Variation Sparse Unmixing for Hyperspectral Remote Sensing Imagery","volume":"59","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3233","DOI":"10.1109\/TGRS.2018.2796069","article-title":"Superpixel-Based Extended Random Walker for Hyperspectral Image Classification","volume":"56","author":"Cui","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"6663","DOI":"10.1109\/TGRS.2015.2445767","article-title":"Classification of Hyperspectral Images by Exploiting Spectral\u2013Spatial Information of Superpixel via Multiple Kernels","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"7770","DOI":"10.1109\/TGRS.2019.2916329","article-title":"Collaborative Representation-Based Multiscale Superpixel Fusion for Hyperspectral Image Classification","volume":"57","author":"Jia","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"6950","DOI":"10.1109\/TGRS.2017.2737037","article-title":"Multimorphological Superpixel Model for Hyperspectral Image Classification","volume":"55","author":"Liu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","first-page":"1","article-title":"Multiscale Superpixel-Based Active Learning for Hyperspectral Image Classification","volume":"19","author":"Lu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4398","DOI":"10.1109\/TGRS.2017.2691906","article-title":"From Subpixel to Superpixel: A Novel Fusion Framework for Hyperspectral Image Classification","volume":"55","author":"Lu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2575","DOI":"10.1109\/TGRS.2017.2647815","article-title":"Superpixel-Based Multitask Learning Framework for Hyperspectral Image Classification","volume":"55","author":"Jia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5847","DOI":"10.1109\/TGRS.2020.2971716","article-title":"Adaptive MultiScale Segmentations for Hyperspectral Image Classification","volume":"58","author":"Leng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/TGRS.2017.2754511","article-title":"Local Binary Pattern-Based Hyperspectral Image Classification with Superpixel Guidance","volume":"56","author":"Jia","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3838","DOI":"10.1109\/TGRS.2018.2813366","article-title":"Hyperspectral Image Classification with Imbalanced Data Based on Orthogonal Complement Subspace Projection","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4355","DOI":"10.1109\/TGRS.2017.2691607","article-title":"Morphologically Decoupled Structured Sparsity for Rotation-Invariant Hyperspectral Image Analysis","volume":"55","author":"Prasad","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1109\/LGRS.2018.2871273","article-title":"Sparse Representation-Based Hyperspectral Image Classification Using Multiscale Superpixels and Guided Filter","volume":"16","author":"Dundar","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1109\/JSTARS.2020.3038456","article-title":"Union of Class-Dependent Collaborative Representation Based on Maximum Margin Projection for Hyperspectral Imagery Classification","volume":"14","author":"Yu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4655","DOI":"10.1109\/TIT.2007.909108","article-title":"Signal Recovery From Random Measurements via Orthogonal Matching Pursuit","volume":"53","author":"Tropp","year":"2008","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3778","DOI":"10.1109\/TGRS.2019.2957135","article-title":"Ensemble Learning for Hyperspectral Image Classification Using Tangent Collaborative Representation","volume":"58","author":"Su","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1109\/JSTARS.2019.2915588","article-title":"Adjacent Superpixel-Based Multiscale spatial\u2013spectral Kernel for Hyperspectral Classification","volume":"12","author":"Sun","year":"2019","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\/14\/3\/681\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:12:05Z","timestamp":1760134325000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/681"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,31]]},"references-count":56,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030681"],"URL":"https:\/\/doi.org\/10.3390\/rs14030681","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,1,31]]}}}