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Title:

Boosting crop classification by hierarchically fusing satellite, rotational, and contextual data

Document type:
Zeitschriftenaufsatz
Author(s):
Valentin Barriere, Martin Claverie, Maja Schneider, Guido Lemoine, Raphaël d’Andrimont
Abstract:
Accurate early-season crop type classification is crucial for the crop production estimation and monitoring of agricultural parcels. However, the complexity of the plant growth patterns and their spatio-temporal variability present significant challenges. While current deep learning-based methods show promise in crop type classification from single- and multi-modal time series, most existing methods rely on a single modality, such as satellite optical remote sensing data or crop rotation pattern...     »
Keywords:
Agriculture; Crop type mapping; Earth Observation; Geospatial Application; Long-Short-Term-Memory; Sentinel-2
Dewey Decimal Classification:
500 Naturwissenschaften
Journal title:
Remote Sensing of Environment
Year:
2024
Journal volume:
305
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.1016/j.rse.2024.114110
WWW:
https://www.sciencedirect.com/science/article/pii/S0034425724001214/pdfft?md5=0370067d2d72493ad5aa894341a56938&pid=1-s2.0-S0034425724001214-main.pdf
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