elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

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

Vaduva, Corina and Danisor, Cosmin and Datcu, Mihai (2018) Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach. Remote Sensing, 10, 1436/1-1436/22. Multidisciplinary Digital Publishing Institute (MDPI). ISSN 2072-4292

[img] PDF
7MB

Official URL: https://www.mdpi.com/2072-4292/10/9/1436

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.

Item URL in elib:https://elib.dlr.de/123442/
Document Type:Article
Title:Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Vaduva, CorinaPolitehnica University of Bucharest, RomaniaUNSPECIFIED
Danisor, CosminUniversity Politehnica BucharestUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Date:September 2018
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:10
Page Range:1436/1-1436/22
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:SAR image time series analysis; Latent Dirichlet Allocation; categories of evolution; PSInSAR data analytics; evolutionary character of Persistent Scatterers
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Dumitru, Corneliu Octavian
Deposited On:28 Nov 2018 15:54
Last Modified:14 Dec 2019 04:26

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.