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Remote Sens. 2018, 10(9), 1436; https://doi.org/10.3390/rs10091436

Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach

1
Research Center for Spatial Information, University “Politehnica” of Bucharest, 061071 Bucharest, Romania
2
Remote Sensing Technology Institute, German Aerospace Center, 82234 Oberpfaffenhofen, Germany
*
Author to whom correspondence should be addressed.
Received: 6 July 2018 / Revised: 31 August 2018 / Accepted: 3 September 2018 / Published: 8 September 2018
(This article belongs to the Special Issue Analysis of Multi-temporal Remote Sensing Images)
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Abstract

Due to the constant increase in Earth Observation (EO) data collections, the monitoring of land cover is facilitated by the temporal diversity of the satellite images datasets. Due to the capacity of Synthetic Aperture Radar (SAR) sensors to operate independently of sunlight and weather conditions, SAR image time series offer the possibility to form a dataset with almost regular temporal sampling. This paper aims at mining the SAR image time series for an analysis of target’s behavior from the perspective of both temporal evolution and coherence. The authors present a two-level analytical approach envisaging the assessment of global (related to perceivable structures on the ground) and local (related to changes occurred within a perceivable structure on the ground) evolution inside the scene. The Latent Dirichlet Allocation (LDA) model is implemented to identify the categories of evolution present in the analyzed scene, while the statistical and coherent proprieties of the dataset’s images are exploited in order to identify the structures with stable electromagnetic response, the so-called Persistent Scatterers (PS). A comparative study of the two algorithms’ classification results is conducted on ERS and Sentinel-1 data. At global scale, the results fit human perception, as most of the points which can be exploited for Persistent Scatterers Interferometry (PS-InSAR) are classified within the same class, referring to stable structures. At local scale, the LDA classification demands for an extended number of classes (defined through a perplexity-based analysis), enabling further differentiation inside the evolutional character of those stable structures. The comparison against the map of detected PS reveals which classes present higher temporal correlation, determining a stable evolutionary character, opening new perspectives for validation of both PS detection and SITS analysis algorithms. View Full-Text
Keywords: SAR image time series analysis; Latent Dirichlet Allocation; categories of evolution; PSInSAR data analytics; evolutionary character of Persistent Scatterers SAR image time series analysis; Latent Dirichlet Allocation; categories of evolution; PSInSAR data analytics; evolutionary character of Persistent Scatterers
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Văduva, C.; Dănișor, C.; Datcu, M. Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach. Remote Sens. 2018, 10, 1436.

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