{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T15:55:09Z","timestamp":1760889309875,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2016,12,16]],"date-time":"2016-12-16T00:00:00Z","timestamp":1481846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation","award":["61175008"],"award-info":[{"award-number":["61175008"]}]},{"name":"The 973 Project","award":["613XX01-2015JD-07"],"award-info":[{"award-number":["613XX01-2015JD-07"]}]},{"DOI":"10.13039\/501100019082","name":"Shanghai Aerospace Science and Technology Innovation Fund","doi-asserted-by":"publisher","award":["SAST201448"],"award-info":[{"award-number":["SAST201448"]}],"id":[{"id":"10.13039\/501100019082","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004750","name":"Aeronautical Science Foundation of China","doi-asserted-by":"publisher","award":["20140157001"],"award-info":[{"award-number":["20140157001"]}],"id":[{"id":"10.13039\/501100004750","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.<\/jats:p>","DOI":"10.3390\/s16122146","type":"journal-article","created":{"date-parts":[[2016,12,16]],"date-time":"2016-12-16T10:55:28Z","timestamp":1481885728000},"page":"2146","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6148-9853","authenticated-orcid":false,"given":"Da","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianxun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21117","DOI":"10.3390\/s141121117","article-title":"Remote Sensing of Ecosystem Health: Opportunities, Challenges, and Future Perspectives","volume":"14","author":"Li","year":"2014","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4301","DOI":"10.1016\/j.rse.2008.07.016","article-title":"The role of environmental context in mapping invasive plants with hyperspectral image data","volume":"112","author":"Andrew","year":"2008","journal-title":"Remote Sens. 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