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 However, with the growing image resolution, pixel-level SITS analysis approaches have been replaced by more eff...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 12; no. 11; p. 1816
Main Authors: Kalinicheva, Ekaterina, Sublime, Jérémie, Trocan, Maria
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.06.2020
MDPI
Series:Advanced Machine Learning for Time Series Remote Sensing Data Analysis
Subjects:
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 Access:Get full text
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Summary: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 However, with the growing image resolution, pixel-level SITS analysis approaches have been replaced by more efficient ones leveraging object-based data representations. Unfortunately, the segmentation of a full time series may be a complicated task as some objects undergo important variations from one image to another and can also appear and disappear. In this paper, we propose an algorithm that performs both segmentation and clustering of SITS. It is achieved by using a compressed SITS representation obtained with a multi-view 3D convolutional autoencoder. First, a unique segmentation map is computed for the whole SITS. Then, the extracted spatio-temporal objects are clustered using their encoded descriptors. The proposed approach was evaluated on two real-life datasets and outperformed the state-of-the-art methods.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs12111816