{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:26:06Z","timestamp":1776101166259,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T00:00:00Z","timestamp":1721433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hyperspectral image (HSI) classification is a vital part of the HSI application field. Since HSIs contain rich spectral information, it is a major challenge to effectively extract deep representation features. In existing methods, although edge data augmentation is used to strengthen the edge representation, a large amount of high-frequency noise is also introduced at the edges. In addition, the importance of different spectra for classification decisions has not been emphasized. Responding to the above challenges, we propose an edge-aware and spectral\u2013spatial feature learning network (ESSN). ESSN contains an edge feature augment block and a spectral\u2013spatial feature extraction block. Firstly, in the edge feature augment block, the edges of the image are sensed, and the edge features of different spectral bands are adaptively strengthened. Then, in the spectral\u2013spatial feature extraction block, the weights of different spectra are adaptively adjusted, and more comprehensive depth representation features are extracted on this basis. Extensive experiments on three publicly available hyperspectral datasets have been conducted, and the experimental results indicate that the proposed method has higher accuracy and immunity to interference compared to state-of-the-art (SOTA) method.<\/jats:p>","DOI":"10.3390\/s24144714","type":"journal-article","created":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T14:45:53Z","timestamp":1721659553000},"page":"4714","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Joint Network of Edge-Aware and Spectral\u2013Spatial Feature Learning for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"24","author":[{"given":"Jianfeng","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China"}]},{"given":"Yu","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Municipal and Environmental Engineering, Heilongjiang Institute of Construction Technology, Harbin 150025, China"}]},{"given":"Yuqi","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China"}]},{"given":"Senlong","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China"}]},{"given":"Cuiping","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China"}]},{"given":"Xiaodong","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,20]]},"reference":[{"key":"ref_1","first-page":"5609318","article-title":"RGB-to-HSV: A frequency-spectrum unfolding network for spectral super-resolution of RGB videos","volume":"62","author":"Zhou","year":"2024","journal-title":"IEEE Trans. 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