Application of Deep Learning Architectures for Satellite Image Time Series Prediction: A Review

Satellite image time series (SITS) is a sequence of satellite images that record a given area at several consecutive times. The aim of such sequences is to use not only spatial information but also the temporal dimension of the data, which is used for multiple real-world applications, such as classi...

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Vydané v:	Remote sensing (Basel, Switzerland) Ročník 13; číslo 23; s. 4822
Hlavní autori:	Moskolaï, Waytehad Rose, Abdou, Wahabou, Dipanda, Albert, Kolyang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.12.2021 MDPI
Predmet:	Algorithms Anomalies Artificial intelligence Artificial neural networks Computer Science data collection Deep learning forecasting hybrids Image segmentation Learning algorithms Machine learning Missing data Multilayers Neural networks Nowcasting Optimization Precipitation prediction Prediction models Recurrent neural networks Remote sensing Reviews satellite image time series Satellite imagery Satellites Sequences Spatial data Time series time series analysis Weather forecasting
ISSN:	2072-4292, 2072-4292
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