scholarly journals SPATIO-TEMPORAL OBJECT STABILITY FOR MONITORING EVOLVING AREAS IN SATELLITE IMAGE TIME SERIES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1273-1280
Author(s):  
C. Tuna ◽  
F. Merciol ◽  
S. Lefèvre
Keyword(s):  
Time Series ◽  
Satellite Image ◽  
Temporal Stability ◽  
Space Time ◽  
Spatial And Temporal Scales ◽  
Temporal Object ◽  
Novel Method ◽  
Spatio Temporal ◽  
Sentinel 2 ◽  
Do So

Abstract. Monitoring observable processes in Satellite Image Time Series (SITS) is one of the crucial way to understand dynamics of our planet that is facing unexpected behaviors due to climate change. In this paper, we propose a novel method to assess the evolution of objects (and especially their surface) through time. To do so, we first build a space-time tree representation of image time series. The so-called space-time tree is a hierarchical representation of an image sequences into a nested set of nodes characterizing the observed regions at multiple spatial and temporal scales. Then, we measure for each node the spatial area occupied at each time sample, and we focus on its evolution through time. We thus define the spatio-temporal stability of each node. We use this attribute to identify and measure changing areas in a remotely-sensed scene. We illustrate the purpose of our method with some experiments in a coastal environment using Sentinel-2 images, and in a flood occurred area with Sentinel-1 images.

scholarly journals CLOUD REMOVAL FROM SENTINEL-2 IMAGE TIME SERIES THROUGH SPARSE RECONSTRUCTION FROM RANDOM SAMPLES

2016 ◽  
Vol XLI-B3 ◽  
pp. 469-473
Author(s):  
D. Cerra ◽  
J. Bieniarz ◽  
R. Müller ◽  
P. Reinartz
Keyword(s):  
Time Series ◽  
Satellite Image ◽  
Reconstruction Error ◽  
Sparse Reconstruction ◽  
Shadow Mask ◽  
Spectral Evolution ◽  
Random Samples ◽  
Restoration Algorithm ◽  
Cloud Removal ◽  
Sentinel 2

In this paper we propose a cloud removal algorithm for scenes within a Sentinel-2 satellite image time series based on synthetisation of the affected areas via sparse reconstruction. For this purpose, a clouds and clouds shadow mask must be given. With respect to previous works, the process has an increased automation degree. Several dictionaries, on the basis of which the data are reconstructed, are selected randomly from cloud-free areas around the cloud, and for each pixel the dictionary yielding the smallest reconstruction error in non-corrupted images is chosen for the restoration. The values below a cloudy area are therefore estimated by observing the spectral evolution in time of the non-corrupted pixels around it. The proposed restoration algorithm is fast and efficient, requires minimal supervision and yield results with low overall radiometric and spectral distortions.

scholarly journals Spatio-temporal reasoning for the classification of satellite image time series

2012 ◽  
Vol 33 (13) ◽  
pp. 1805-1815 ◽  
Author(s):  
François Petitjean ◽  
Camille Kurtz ◽  
Nicolas Passat ◽  
Pierre Gançarski
Keyword(s):  
Time Series ◽  
Satellite Image ◽  
Temporal Reasoning ◽  
Spatio Temporal

scholarly journals Extracting multilayer networks from Sentinel-2 satellite image time series

2020 ◽  
Vol 8 (S1) ◽  
pp. S26-S42 ◽  
Author(s):  
Roberto Interdonato ◽  
Raffaele Gaetano ◽  
Danny Lo Seen ◽  
Mathieu Roche ◽  
Giuseppe Scarpa
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Image ◽  
European Space Agency ◽  
Image Data ◽  
Earth Observation ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Case Scenario ◽  
Sentinel 2

AbstractNowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is the Sentinel-2 Earth Observation mission, developed by the European Space Agency as part of the Copernicus Programme, which supplies images from the whole planet at high spatial resolution (up to 10 m) with unprecedented revisit time (every 5 days at the equator). In this data-rich scenario, the remote sensing community is showing a growing interest toward modern supervised machine learning techniques (e.g., deep learning) to perform information extraction, often underestimating the need for reference data that this framework implies. Conversely, few attention is being devoted to the use of network analysis techniques, which can provide a set of powerful tools for unsupervised information discovery, subject to the definition of a suitable strategy to build a network-like representation of image data. The aim of this work is to provide clues on how Satellite Image Time Series can be profitably represented using complex network models, by proposing a methodology to build a multilayer network from such data. This is the first work to explore the possibility to exploit this model in the remote sensing domain. An example of community detection over the provided network in a real-case scenario for the mapping of complex land use systems is also presented, to assess the potential of this approach.

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