{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T14:33:29Z","timestamp":1775226809487,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"publisher","award":["2022NSFSC0536"],"award-info":[{"award-number":["2022NSFSC0536"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"publisher","award":["2021HZSY0071"],"award-info":[{"award-number":["2021HZSY0071"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"publisher","award":["22kE018"],"award-info":[{"award-number":["22kE018"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Project of Wenzhou Key Laboratory Foundation, China","award":["2022NSFSC0536"],"award-info":[{"award-number":["2022NSFSC0536"]}]},{"name":"the Project of Wenzhou Key Laboratory Foundation, China","award":["2021HZSY0071"],"award-info":[{"award-number":["2021HZSY0071"]}]},{"name":"the Project of Wenzhou Key Laboratory Foundation, China","award":["22kE018"],"award-info":[{"award-number":["22kE018"]}]},{"name":"Doctoral Initiation Program of China West Normal University","award":["2022NSFSC0536"],"award-info":[{"award-number":["2022NSFSC0536"]}]},{"name":"Doctoral Initiation Program of China West Normal University","award":["2021HZSY0071"],"award-info":[{"award-number":["2021HZSY0071"]}]},{"name":"Doctoral Initiation Program of China West Normal University","award":["22kE018"],"award-info":[{"award-number":["22kE018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, the rapid development of deep learning has greatly improved the performance of image classification. However, a central problem in hyperspectral image (HSI) classification is spectral uncertainty, where spectral features alone cannot accurately and robustly identify a pixel point in a hyperspectral image. This paper presents a novel HSI classification network called MS-RPNet, i.e., multiscale superpixelwise RPNet, which combines superpixel-based S3-PCA with two-dimensional singular spectrum analysis (2D-SSA) based on the Random Patches Network (RPNet). The proposed frame can not only take advantage of the data-driven method, but can also apply S3-PCA to efficiently consider more global and local spectral knowledge at the super-pixel level. Meanwhile, 2D-SSA is used for noise removal and spatial feature extraction. Then, the final features are obtained by random patch convolution and other steps according to the cascade structure of RPNet. The layered extraction superimposes the different sparial information into multi-scale spatial features, which complements the features of various land covers. Finally, the final fusion features are classified by SVM to obtain the final classification results. The experimental results in several HSI datasets demonstrate the effectiveness and efficiency of MS-RPNet, which outperforms several current state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs15133402","type":"journal-article","created":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T00:37:28Z","timestamp":1688517448000},"page":"3402","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Hyperspectral Image Classification Based on Fusing S3-PCA, 2D-SSA and Random Patch Network"],"prefix":"10.3390","volume":"15","author":[{"given":"Huayue","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer, China West Normal University, Nanchong 637002, China"}]},{"given":"Tingting","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer, China West Normal University, Nanchong 637002, China"}]},{"given":"Tao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer, China West Normal University, Nanchong 637002, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6524-6760","authenticated-orcid":false,"given":"Wu","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China"},{"name":"The State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,4]]},"reference":[{"key":"ref_1","first-page":"45","article-title":"Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods","volume":"31","author":"Tuia","year":"2013","journal-title":"IEEE Signal Process. 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