{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:48:38Z","timestamp":1772300918561,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T00:00:00Z","timestamp":1684886400000},"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":["42271409"],"award-info":[{"award-number":["42271409"]}],"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":["145209122"],"award-info":[{"award-number":["145209122"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds in Heilongjiang Provincial Universities","award":["42271409"],"award-info":[{"award-number":["42271409"]}]},{"name":"Fundamental Research Funds in Heilongjiang Provincial Universities","award":["145209122"],"award-info":[{"award-number":["145209122"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image (HSI) classification is one of the most crucial tasks in remote sensing processing. The attention mechanism is preferable to a convolutional neural network (CNN), due to its superior ability to express information during HSI processing. Recently, numerous methods combining CNNs and attention mechanisms have been applied in HSI classification. However, it remains a challenge to achieve high-accuracy classification by fully extracting effective features from HSIs under the conditions of limited labeled samples. In this paper, we design a novel HSI classification network based on multiscale hybrid networks and attention mechanisms. The network consists of three subnetworks: a spectral-spatial feature extraction network, a spatial inverted pyramid network, and a classification network, which are employed to extract spectral-spatial features, to extract spatial features, and to obtain classification results, respectively. The multiscale fusion network and attention mechanisms complement each other by capturing local and global features separately. In the spatial pyramid network, multiscale spaces are formed through down-sampling, which can reduce redundant information while retaining important information. The structure helps the network better capture spatial features at different scales, and to improve classification accuracy. Experimental results on various public HSI datasets demonstrate that the designed network is extremely competitive compared to current advanced approaches, under the condition of insufficient samples.<\/jats:p>","DOI":"10.3390\/rs15112720","type":"journal-article","created":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T02:00:55Z","timestamp":1684980055000},"page":"2720","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Hyperspectral Image Classification Based on Multiscale Hybrid Networks and Attention Mechanisms"],"prefix":"10.3390","volume":"15","author":[{"given":"Haizhu","family":"Pan","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering, Qiqihar University, Qiqihar 161000, China"},{"name":"Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihar University, Qiqihar 161000, China"}]},{"given":"Xiaoyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Qiqihar University, Qiqihar 161000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6652-3611","authenticated-orcid":false,"given":"Haimiao","family":"Ge","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Qiqihar University, Qiqihar 161000, China"},{"name":"Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihar University, Qiqihar 161000, China"}]},{"given":"Moqi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Qiqihar University, Qiqihar 161000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5877-1762","authenticated-orcid":false,"given":"Cuiping","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Telecommunication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2011.02.030","article-title":"Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends","volume":"117","author":"Weng","year":"2012","journal-title":"Remote Sens. 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