A time-series-based spatiotemporal fusion network (TSSTFN) was designed to generate TSSTFN-NDVI during critical phenological periods for finer-scale crop ...
The satellite-normalized difference vegetation index (NDVI) obtained during periods of vigorous crop growth is important for crop species identification.
Improving the mapping of crop types in the Midwestern U.S. by fusing Landsat and MODIS satellite data · Agricultural and Food Sciences, Environmental Science.
In this paper, we propose a novel spatio-temporal multi-level attention method, named as STMA, for crop mapping using time-series SAR imagery in an end-to-end ...
The aim of this paper is to map agricultural crops by classifying satellite image time series. Domain experts in agriculture work with crop type labels that ...
from publication: Time-Series-Based Spatiotemporal Fusion Network for Improving Crop Type Mapping | Crop mapping is vital in ensuring food production ...
Jan 23, 2024 · In this paper, based on deep learning, we proposed a residual convolutional neural network (Res-CNN) model to improve the fusion result considerably with brand ...
BreizhCrops is a novel benchmark dataset for the supervised classification of field crops from satellite time series in the region of Brittany, ...
Missing: Spatiotemporal | Show results with:Spatiotemporal
Mar 29, 2023 · Deep learning methods have achieved promising results in crop mapping using satellite image time series. A challenge still remains on how to ...
Jul 3, 2023 · Improved temporal resolution can better capture short-term changes of phenological growth stages in crops, making them more separable.