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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Bibliographic Details
Published in:	Remote sensing (Basel, Switzerland) Vol. 13; no. 23; p. 4822
Main Authors:	Moskolaï, Waytehad Rose, Abdou, Wahabou, Dipanda, Albert, Kolyang
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.12.2021 MDPI
Subjects:	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
Online Access:	Get full text
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