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Dear Colleagues,

Recently, long radar time series are becoming more and more accessible to treatments, mainly thanks to the Sentinel 1 satellites. Until then, these time-series were particularly useful for measuring deformations by differential interferometry, one of the critical applications for which polarimetry has demonstrated a significant advantage to select permanent scatterers.

However, radar images are also particularly useful for detecting changes, and access to time dimension enlarges the potential uses, whether for urban sprawl monitoring, crop monitoring, pipelines monitoring, flood mapping, or maritime applications. Here again, polarimetry will play a crucial role, whether for pre-processing, improving the performance of current algorithms, or retrospective analysis.

With this special issue, we compile state-of-the-art research that specifically addresses the benefits of Polarimetry in SAR-stime series, called PolTimeSAR. Review contributions are welcomed as well as works proposing an original use of full or partial polarimetry for change detection in time series, measurement concepts/sensors/constellations, or new purposes.

Dr. Elise Colin Koeniguer
Guest Editor

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Keywords

SAR (Synthetic Aperture Radar)
polarimetry
time-series
change detection
activity
surveillance

Published Papers (2 papers)

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Research

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21 pages, 8151 KiB

Open AccessFeature PaperArticle

Detecting Ephemeral Objects in SAR Time-Series Using Frozen Background-Based Change Detection

by Thibault Taillade, Laetitia Thirion-Lefevre and Régis Guinvarc’h

Remote Sens. 2020, 12(11), 1720; https://doi.org/10.3390/rs12111720 - 27 May 2020

Cited by 5 | Viewed by 3021

Abstract

Change detection (CD) in SAR (Synthethic Aperture Radar) images has been widely studied in recent years and has become increasingly attractive due to the growth of available datasets. The potential of CD has been shown in different fields, including disaster monitoring and military applications. Access to multi-temporal SAR images of the same scene is now possible, and therefore we can improve the performance and the interpretation of CD. Apart from specific SAR campaign measurements, the ground truth of the scene is usually unknown or only partially known when dealing with open data. This is a critical issue when the purpose is to detect targets, such as vehicles or ships. Indeed, typical change detection methods can only provide relative changes; the actual number of targets on each day cannot be determined. Ideally, this change detection should occur between a target-free image and one with the objects of interest. To do so, we propose to benefit from pixels’ intrinsic temporal behavior to compute a frozen background reference (FBR) image and perform change detection from this reference image. We will then consider that the scene consists only of immobile objects (e.g., buildings and trees) and removable objects that can appear and disappear from acquisition to another (e.g., cars and ships). Our FBR images will, therefore, aim to estimate the immobile background of the scene to obtain, after change detection, the exact amount of targets present on each day. This study was conducted first with simulated SAR data for different number of acquisition dates and Signal-to-Noise Ratio (SNR). We presented an application in the region of Singapore to estimate the number of ships in the study area for each acquisition. Full article

(This article belongs to the Special Issue PolTimeSAR: Polarimetric Time-Series SAR Images: Applications in Change Detection)

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11 pages, 7907 KiB

Open AccessLetter

Wishart-Based Adaptive Temporal Filtering of Polarimetric SAR Imagery

by Morton J. Canty, Allan A. Nielsen, Henning Skriver and Knut Conradsen

Remote Sens. 2020, 12(15), 2454; https://doi.org/10.3390/rs12152454 - 30 Jul 2020

Cited by 2 | Viewed by 7961

Abstract

Temporal filtering for speckle reduction of polarimetric SARimages is described. The method is based on a sequential complex Wishart-based change detection algorithm which is applied to polarized SAR imagery, including the dual-polarization intensity data archived on the Google Earth Engine (GEE). Software for convenient application and analysis is presented. Results compare favorably with, and improve upon, standard spatial and temporal filters for speckle reduction. Full article

(This article belongs to the Special Issue PolTimeSAR: Polarimetric Time-Series SAR Images: Applications in Change Detection)

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