Benutzer: Gast  Login
Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Stichworte:
Agriculture; Crop type mapping; Earth Observation; Geospatial Application; Long-Short-Term-Memory; Sentinel-2
Dewey Dezimalklassifikation:
500 Naturwissenschaften
Zeitschriftentitel:
Remote Sensing of Environment
Jahr:
2024
Band / Volume:
305
Reviewed:
ja
Sprache:
en
Volltext / 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
 BibTeX