{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:39:11Z","timestamp":1777696751074,"version":"3.51.4"},"reference-count":47,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Data Analysis: An International Journal"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:p>Joint spectral-spatial feature extraction has been proven to be the most effective part of hyperspectral image (HSI) classification. But, due to the mixing of informative and noisy bands in HSI, joint spectral-spatial feature extraction using convolutional neural network (CNN) may lead to information loss and high computational cost. More specifically, joint spectral-spatial feature extraction from excessive bands may cause loss of spectral information due to the involvement of convolution operation on non-informative spectral bands. Therefore, we propose a simple yet effective deep learning model, named deep hierarchical spectral-spatial feature fusion (DHSSFF), where spectral-spatial features are exploited separately to reduce the information loss and fuse the deep features to learn the semantic information. It makes use of abundant spectral bands and few informative bands of HSI for spectral and spatial feature extraction, respectively. The spectral and spatial features are extracted through 1D CNN and 3D CNN, respectively. To validate the effectiveness of our model, the experiments have been performed on five well-known HSI datasets. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods and achieved 99.17%, 98.84%, 98.70%, 99.18%, and 99.24% overall accuracy on Kennedy Space Center, Botswana, Indian Pines, University of Pavia, and Salinas datasets, respectively.<\/jats:p>","DOI":"10.3233\/ida-230927","type":"journal-article","created":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T10:55:40Z","timestamp":1724151340000},"page":"385-407","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep hierarchical spectral-spatial feature fusion for hyperspectral image classification based on convolutional neural network"],"prefix":"10.1177","volume":"29","author":[{"given":"Somenath","family":"Bera","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, School of Technology, Institute of Technology, Nirma University,  Ahmedabad, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naushad","family":"Varish","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, GITAM University, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syed irfan","family":"Yaqoob","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering (AIT), Chandigarh University, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mudassir","family":"Rafi","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, SRM University, Amaravati, India"},{"name":"Department of Computer Science, College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vimal K.","family":"Shrivastava","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,4,2]]},"reference":[{"issue":"1","key":"e_1_3_3_2_2","first-page":"85","article-title":"A review of current issues in the integration of GIS and remote sensing data","volume":"10","author":"Wilkinson G.","year":"1996","unstructured":"Wilkinson G., A review of current issues in the integration of GIS and remote sensing data, International Journal of Geographical Information Science 10(1) (1996), 85\u2013101.","journal-title":"International Journal of Geographical Information Science"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-13959-w"},{"key":"e_1_3_3_4_2","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.isprsjprs.2016.12.009","article-title":"A survey of landmine detection using hyperspectral imaging","volume":"124","author":"Makki I.","year":"2017","unstructured":"Makki I., Younes R., Francis C., Bianchi T., Zucchetti M., A survey of landmine detection using hyperspectral imaging, ISPRS Journal of Photogrammetry and Remote Sensing 124 (2017), 40\u201353.","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"issue":"12","key":"e_1_3_3_5_2","doi-asserted-by":"crossref","first-page":"2875","DOI":"10.1016\/j.rse.2010.07.005","article-title":"Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data","volume":"114","author":"Rhee J.","year":"2010","unstructured":"Rhee J., Im J., Carbone G.J., Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data, Remote Sensing of Environment 114(12) (2010), 2875\u20132887.","journal-title":"Remote Sensing of Environment"},{"issue":"1","key":"e_1_3_3_6_2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1080\/15481603.2015.1114199","article-title":"A support vector machine classifier based on a new kernel function model for hyperspectral data","volume":"53","author":"Lin Z.","year":"2016","unstructured":"Lin Z., Yan L., A support vector machine classifier based on a new kernel function model for hyperspectral data, GIScience & Remote Sensing 53(1) (2016), 85\u2013101.","journal-title":"GIScience & Remote Sensing"},{"issue":"23","key":"e_1_3_3_7_2","doi-asserted-by":"crossref","first-page":"7053","DOI":"10.1007\/s00500-016-2247-2","article-title":"SVM or deep learning? 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