{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T01:32:54Z","timestamp":1779327174449,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"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 classification remains challenging despite its potential due to the high dimensionality of the data and its limited spatial resolution. To address the limited data samples and less spatial resolution issues, this research paper presents a two-scale module-based CTNet (convolutional transformer network) for the enhancement of spatial and spectral features. In the first module, a virtual RGB image is created from the HSI dataset to improve the spatial features using a pre-trained ResNeXt model trained on natural images, whereas in the second module, PCA (principal component analysis) is applied to reduce the dimensions of the HSI data. After that, spectral features are improved using an EAVT (enhanced attention-based vision transformer). The EAVT contained a multiscale enhanced attention mechanism to capture the long-range correlation of the spectral features. Furthermore, a joint module with the fusion of spatial and spectral features is designed to generate an enhanced feature vector. Through comprehensive experiments, we demonstrate the performance and superiority of the proposed approach over state-of-the-art methods. We obtained AA (average accuracy) values of 97.87%, 97.46%, 98.25%, and 84.46% on the PU, PUC, SV, and Houston13 datasets, respectively.<\/jats:p>","DOI":"10.3390\/s24062016","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T11:37:22Z","timestamp":1711021042000},"page":"2016","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Advancing Hyperspectral Image Analysis with CTNet: An Approach with the Fusion of Spatial and Spectral Features"],"prefix":"10.3390","volume":"24","author":[{"given":"Dhirendra Prasad","family":"Yadav","sequence":"first","affiliation":[{"name":"Department of Computer Engineering & Applications, G.L.A. University, Mathura 281406, Uttar Pradesh, India"},{"name":"Department of Computer Engineering, NIT Meghalaya, Shillong 793001, Meghalaya, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deepak","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, NIT Meghalaya, Shillong 793001, Meghalaya, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anand Singh","family":"Jalal","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering & Applications, G.L.A. University, Mathura 281406, Uttar Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3400-3504","authenticated-orcid":false,"given":"Bhisham","family":"Sharma","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7796-2898","authenticated-orcid":false,"given":"Julian L.","family":"Webber","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), Doha Area, 7th Ring Road, Kuwait City 13133, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abolfazl","family":"Mehbodniya","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), Doha Area, 7th Ring Road, Kuwait City 13133, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Hyperspectral image classification: Potentials, challenges, and future directions","volume":"2022","author":"Datta","year":"2022","journal-title":"Comput. 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