{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:34:44Z","timestamp":1760142884051,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41672358","2019QZKK0707","41988101-01"],"award-info":[{"award-number":["41672358","2019QZKK0707","41988101-01"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research","award":["41672358","2019QZKK0707","41988101-01"],"award-info":[{"award-number":["41672358","2019QZKK0707","41988101-01"]}]},{"name":"Basic Science Centre for Tibetan Plateau Earth System","award":["41672358","2019QZKK0707","41988101-01"],"award-info":[{"award-number":["41672358","2019QZKK0707","41988101-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, deep learning-based classification methods for hyperspectral images (HSIs) have gained widespread popularity in fields such as agriculture, environmental monitoring, and geological exploration. This is owing to their ability to automatically extract features and deliver outstanding performance. This study provides a new Dilated Spectral\u2013Spatial Gaussian Transformer Net (DSSGT) model. The DSSGT model incorporates dilated convolutions as shallow feature extraction units, which allows for an expanded receptive field while maintaining computational efficiency. We integrated transformer architecture to effectively capture feature relationships and generate deep fusion features, thereby enhancing classification accuracy. We used consecutive dilated convolutional layers to extract joint low-level spectral\u2013spatial features. We then introduced Gaussian Weighted Pixel Embedding blocks, which leverage Gaussian weight matrices to transform the joint features into pixel-level vectors. By combining the features of each pixel with its neighbouring pixels, we obtained pixel-level representations that are more expressive and context-aware. The transformed vector matrix was fed into the transformer encoder module, enabling the capture of global dependencies within the input data and generating higher-level fusion features with improved expressiveness and discriminability. We evaluated the proposed DSSGT model using five hyperspectral image datasets through comparative experiments. Our results demonstrate the superior performance of our approach compared to those of current state-of-the-art methods, providing compelling evidence of the DSSGT model\u2019s effectiveness.<\/jats:p>","DOI":"10.3390\/rs16020287","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T03:21:41Z","timestamp":1704943301000},"page":"287","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Dilated Spectral\u2013Spatial Gaussian Transformer Net for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhenbei","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7018-3971","authenticated-orcid":false,"given":"Shuo","family":"Wang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying & Mapping, Beijing 100036, China"}]},{"given":"Weilin","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"ref_1","first-page":"139","article-title":"Review of Hyperspectral Imaging in Quality and Safety Inspections of Agricultural and Poultry Products","volume":"36","author":"Liu","year":"2005","journal-title":"Trans. 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