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On Change Detection for Polsar Image Time Series: A New Clustering Approach

Abstract : Change detection for radar image time series is an important task that can help to monitor deforestation and global warming consequences. We present a method to detect changes in time for Polarimetric SAR images based on a clustering approach. The first step provides a segmentation for each image and then one detects changes by monitoring the resulting labels. This work is based on a robust clustering algorithm that increases the flexibility in the segmentation stage. We report the outcome of our method when it is tested on simulated and real Polarimetric SAR data. The change detection results are promising when comparing our performance to that of other standard methods.
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Contributor : Frédéric Pascal Connect in order to contact the contributor
Submitted on : Friday, November 19, 2021 - 3:27:15 PM
Last modification on : Tuesday, March 29, 2022 - 4:01:36 AM



Violeta Roizman, Guillaume Ginolhac, Matthieu Jonckheere, Frédéric Pascal. On Change Detection for Polsar Image Time Series: A New Clustering Approach. 2021 IEEE Statistical Signal Processing Workshop (SSP), Jul 2021, Rio de Janeiro, Brazil. pp.56-60, ⟨10.1109/SSP49050.2021.9513856⟩. ⟨hal-03436869⟩



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