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Investigation on the potential of hyperspectral and Sentinel-2 data for land-cover / land-use classification

Weinmann, M.; Maier, P. M.; Florath, J.; Weidner, U.

The automated analysis of large areas with respect to land-cover and land-use is nowadays typically performed based on the use of hyperspectral or multispectral data acquired from airborne or spaceborne platforms. While hyperspectral data offer a more detailed description of the spectral properties of the Earth’s surface and thus a great potential for a variety of applications, multispectral data are less expensive and available in shorter time intervals which allows for time series analyses. Particularly with the recent availability of multispectral Sentinel-2 data, it seems desirable to have a comparative assessment of the potential of both types of data for land-cover and land-use classification. In this paper, we focus on such a comparison and therefore involve both types of data. On the one hand, we focus on the potential of hyperspectral data and the commonly applied techniques for data-driven dimensionality reduction or feature selection based on these hyperspectral data. On the other hand, we aim to reason about the potential of Sentinel-2 data and therefore transform the acquired hyperspectral data to Sentinel-2-like data. For performance evaluation, we provide classification results achieved with the different types of data for two standard benchmark datasets representing an urban area and an agricultural area, respectively.

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Verlagsausgabe §
DOI: 10.5445/IR/1000088371
Veröffentlicht am 10.12.2018
DOI: 10.5194/isprs-annals-IV-1-155-2018
Zitationen: 3
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Bauingenieur-, Geo- und Umweltwissenschaften (BGU)
Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2018
Sprache Englisch
Identifikator ISSN: 2194-9042
KITopen-ID: 1000088371
Erschienen in 2018 ISPRS Technical Commission I Midterm Symposium on Innovative Sensing - From Sensors to Methods and Applications; Karlsruhe; Germany; 10 October 2018 through 12 October 2018. Ed.: S. Hinz
Verlag Curran
Seiten 155-162
Serie ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; 4-1
Schlagwörter Classification, Land-cover/Land-use, Hyperspectral Data, Dimensionality Reduction, Feature Selection, Multispectral Data, Sentinel-2 Data
Nachgewiesen in Scopus
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