Unsupervised Satellite Image Time Series Clustering Using Object-Based Approaches and 3D Convolutional Autoencoder

Nowadays, satellite image time series (SITS) analysis has become an indispensable part of many research projects as the quantity of freely available remote sensed data increases every day. However, with the growing image resolution, pixel-level SITS analysis approaches have been replaced by more eff...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 12; H. 11; S. 1816
Hauptverfasser:	Kalinicheva, Ekaterina, Sublime, Jérémie, Trocan, Maria
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.06.2020 MDPI
Schriftenreihe:	Advanced Machine Learning for Time Series Remote Sensing Data Analysis
Schlagworte:	3D convolutional network Algorithms Artificial Intelligence autoencoder Clustering Computer Science Computer Vision and Pattern Recognition data collection Datasets Image processing Image resolution Image segmentation Methods Object recognition Remote sensing Representations Research projects satellite image time series Satellite imagery segmentation Similarity measures Time series time series analysis unsupervised learning Urban areas Vegetation segmentation satellite image time series clustering autoencoder 3D convolutional network unsupervised learning
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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