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Review on Convolutional Neural Networks (CNN) in vegetation remote sensing

Kattenborn, Teja; Leitloff, J.; Schiefer, Felix; Hinz, S.

Abstract:
Identifying and characterizing vascular plants in time and space is required in various disciplines, e.g. in forestry, conservation and agriculture. Remote sensing emerged as a key technology revealing both spatial and temporal vegetation patterns. Harnessing the ever growing streams of remote sensing data for the increasing demands on vegetation assessments and monitoring requires efficient, accurate and flexible methods for data analysis. In this respect, the use of deep learning methods is trend-setting, enabling high predictive accuracy, while learning the relevant data features independently in an end-to-end fashion. Very recently, a series of studies have demonstrated that the deep learning method of Convolutional Neural Networks (CNN) is very effective to represent spatial patterns enabling to extract a wide array of vegetation properties from remote sensing imagery. This review introduces the principles of CNN and distils why they are particularly suitable for vegetation remote sensing. The main part synthesizes current trends and developments, including considerations about spectral resolution, spatial grain, different sensors types, modes of reference data generation, sources of existing reference data, as well as CNN approaches and architectures. ... mehr

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Preprint §
DOI: 10.5445/IR/1000128705
Frei zugänglich ab 01.04.2022
Originalveröffentlichung
DOI: 10.1016/j.isprsjprs.2020.12.010
Scopus
Zitationen: 1
Dimensions
Zitationen: 1
Zugehörige Institution(en) am KIT Institut für Geographie und Geoökologie (IFGG)
Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 31.03.2021
Sprache Englisch
Identifikator ISSN: 0924-2716
KITopen-ID: 1000128705
Erschienen in ISPRS journal of photogrammetry and remote sensing
Verlag Elsevier
Band 173
Seiten 24–49
Schlagwörter Convolutional Neural Networks (CNN), Deep learning, Vegetation, Plants, Remote sensing, Earth observation
Nachgewiesen in Dimensions
Scopus
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