{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T01:10:05Z","timestamp":1769821805220,"version":"3.49.0"},"reference-count":50,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62176259"],"award-info":[{"award-number":["62176259"]}]},{"name":"National Natural Science Foundation of China","award":["61976215"],"award-info":[{"award-number":["61976215"]}]},{"name":"National Natural Science Foundation of China","award":["BE2022095"],"award-info":[{"award-number":["BE2022095"]}]},{"name":"National Natural Science Foundation of China","award":["KJ2021A1119"],"award-info":[{"award-number":["KJ2021A1119"]}]},{"name":"Key Research and Development Program of Jiangsu Province","award":["62176259"],"award-info":[{"award-number":["62176259"]}]},{"name":"Key Research and Development Program of Jiangsu Province","award":["61976215"],"award-info":[{"award-number":["61976215"]}]},{"name":"Key Research and Development Program of Jiangsu Province","award":["BE2022095"],"award-info":[{"award-number":["BE2022095"]}]},{"name":"Key Research and Development Program of Jiangsu Province","award":["KJ2021A1119"],"award-info":[{"award-number":["KJ2021A1119"]}]},{"DOI":"10.13039\/501100010814","name":"Key University Natural Science Research Program of Anhui Province","doi-asserted-by":"publisher","award":["62176259"],"award-info":[{"award-number":["62176259"]}],"id":[{"id":"10.13039\/501100010814","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010814","name":"Key University Natural Science Research Program of Anhui Province","doi-asserted-by":"publisher","award":["61976215"],"award-info":[{"award-number":["61976215"]}],"id":[{"id":"10.13039\/501100010814","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010814","name":"Key University Natural Science Research Program of Anhui Province","doi-asserted-by":"publisher","award":["BE2022095"],"award-info":[{"award-number":["BE2022095"]}],"id":[{"id":"10.13039\/501100010814","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010814","name":"Key University Natural Science Research Program of Anhui Province","doi-asserted-by":"publisher","award":["KJ2021A1119"],"award-info":[{"award-number":["KJ2021A1119"]}],"id":[{"id":"10.13039\/501100010814","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Labeled hyperspectral image (HSI) information is commonly difficult to acquire, so the lack of valid labeled data becomes a major puzzle for HSI classification. Semi-supervised methods can efficiently exploit unlabeled and labeled data for classification, which is highly valuable. Graph-based semi-supervised methods only focus on HSI local or global data and cannot fully utilize spatial\u2013spectral information; this significantly limits the performance of classification models. To solve this problem, we propose an adaptive global\u2013local feature fusion (AGLFF) method. First, the global high-order and local graphs are adaptively fused, and their weight parameters are automatically learned in an adaptive manner to extract the consistency features. The class probability structure is then used to express the relationship between the fused feature and the categories and to calculate their corresponding pseudo-labels. Finally, the fused features are imported into the broad learning system as weights, and the broad expansion of the fused features is performed with the weighted broad network to calculate the model output weights. Experimental results from three datasets demonstrate that AGLFF outperforms other methods.<\/jats:p>","DOI":"10.3390\/rs16111918","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T08:36:22Z","timestamp":1716798982000},"page":"1918","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Hyperspectral Image Classification Based on Adaptive Global\u2013Local Feature Fusion"],"prefix":"10.3390","volume":"16","author":[{"given":"Chunlan","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"School of Electronics and Electrical Engineering, Bengbu University, Bengbu 233030, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1512-0449","authenticated-orcid":false,"given":"Yi","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Xuesong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Yuhu","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,27]]},"reference":[{"key":"ref_1","first-page":"5503005","article-title":"Semisupervised hyperspectral band selection based on dual-constrained low-rank representation","volume":"19","author":"Yu","year":"2022","journal-title":"IEEE Geosci. 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