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Special Issue "Recent Advances in Polarimetric SAR Interferometry"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 June 2017)

Special Issue Editors

Guest Editor
Prof. Dr. Irena Hajnsek

1. Swiss Federal Institute of Technology Zurich (ETH), Institute of Environmental Engineering, HIF D28.1, Stefano-Franscini Platz 3, CH-8093 Zurich, Switzerland
2. German Aerospace Center, Microwaves and Radar Institute, Department: Radar Concepts, Research Group: Pol-InSAR, P.O. Box 1116, D-82234 Wessling, Germany
Website1 | Website2 | E-Mail
Phone: +41/(0)44-633-7455/+49/(0)8153-28-2363
Fax: +49/(0)8153-28-1135
Interests: radar remote sensing of the Earth’s surface; polarimetric and interferometric data processing and analysis for different environmental applications
Guest Editor
Dr. Klaus Scipal

European Space Agency, Earth Observation Mission Science Division, ESTEC, Keplerlaan 1, NL-2201 AZ Noordwijk, The Netherlands
E-Mail
Phone: +31-71-565-5836
Interests: radar remote sensing of the Earth’s surface; satellite system design; bio/geophysical parameter retrieval; data assimilation
Guest Editor
Dr. Pascale Dubois-Fernandez

ONERA, the French Aerospace Lab, Department of Electromagnetism and Radar, Center of Salon de Provence, BA701, 13661 Salon Air Cedex, France
E-Mail
Phone: +33-490170127
Interests: radar remote sensing of the Earth’s surface; polarimetric, interferometric and tomographic data processing; forest mapping
Guest Editor
Prof. Dr. Juan Manuel Lopez-Sanchez

DFISTS - IUII, Universidad de Alicante, P.O. Box 99, E-03080 Alicante, Spain
Website | E-Mail
Phone: +34 965909597
Fax: +34 965909750
Interests: polarimetry; interferometry; polarimetric SAR interferometry; agriculture; subsidence
Guest Editor
Dr. Torbjorn Eltoft

CIRFA, Department of Physics and Technology, UiT—The Arctic University of Norway, Postbox 6050 Langnes, 9037 Tromsø, Norway
Website | E-Mail
Phone: +47-77645184/+47-95007345

Special Issue Information

Dear Colleagues,

The introduction of polarimetric SAR interferometry (Pol-InSAR) at the end of the 1990s was a decisive step towards developing remote sensing applications relevant to forestry. Pol-InSAR is based on the coherent combination of SAR interferograms for different polarisations. On the one hand, SAR interferograms are sensitive to the spatial diversity of vegetation’s vertical structure and allow precise measurement of the scattering centre. On the other, the polarimetric radar signature is sensitive to the shape, orientation and dielectric properties of the scatterers and facilitates the identification and/or separation of scattering mechanisms in natural media. With polarimetric SAR interferometry, the complementary sensitivities of these two measurements are combined coherently, allowing the quantitative determination of relevant (structure) parameters from SAR measurements. Today, Pol-InSAR is an established technique, allowing investigation of the 3-D structure of natural volume scatterers and being applied to a broad domain of application (forestry, agriculture, cryosphere, etc.). Several new techniques have been developed in this domain in terms of data processing and model inversions, as well as extensions have been considered to multi-baseline modes providing an increased observation space. In this Special Issue we like to collect contributions talking the advance in this domain.

We would like to invite you to submit articles about your recent research with respect to the following topics:

  • Polarimetric SAR: Methods, models and inversion
  • Polarimetric SAR applied to different applications
  • Polarimetric SAR Interferometry: Methods, models and inversion
  • Polarimetric SAR Inteferometry applied to different applications
  • Multi-baseline polarimeric SAR interferometry (Polarimetric Tomography)
  • Satellite missions employing polarimetric SAR interferometry
  • Review articles covering one or more of these topics are also welcome.

Authors are required to check and follow specific Instructions to Authors, see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf.

Prof. Dr. Irena Hajnsek
Dr. Klaus Scipal
Dr. Pascale Dubois-Fernandez
Prof. Dr. Juan Manuel Lopez Sanchez
Dr. Torbjorn Eltoft
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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Open AccessArticle Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography
Remote Sens. 2017, 9(12), 1229; doi:10.3390/rs9121229
Received: 14 September 2017 / Revised: 14 September 2017 / Accepted: 2 November 2017 / Published: 28 November 2017
PDF Full-text (39364 KB) | HTML Full-text | XML Full-text
Abstract
Synthetic Aperture Radar Tomography (TomoSAR) allows the reconstruction of the 3D reflectivity of natural volume scatterers such as forests, thus providing an opportunity to infer structure information in 3D. In this paper, the potential of TomoSAR data at L-band to monitor temporal variations
[...] Read more.
Synthetic Aperture Radar Tomography (TomoSAR) allows the reconstruction of the 3D reflectivity of natural volume scatterers such as forests, thus providing an opportunity to infer structure information in 3D. In this paper, the potential of TomoSAR data at L-band to monitor temporal variations of forest structure is addressed using simulated and experimental datasets. First, 3D reflectivity profiles were extracted by means of TomoSAR reconstruction based on a Compressive Sensing (CS) approach. Next, two complementary indices for the description of horizontal and vertical forest structure were defined and estimated by means of the distribution of local maxima of the reconstructed reflectivity profiles. To assess the sensitivity and consistency of the proposed methodology, variations of these indices for different types of forest changes in simulated as well as in real scenarios were analyzed and assessed against different sources of reference data: airborne Lidar measurements, high resolution optical images, and forest inventory data. The forest structure maps obtained indicated the potential to distinguish between different forest stages and the identification of different types of forest structure changes induced by logging, natural disturbance, or forest management. Full article
(This article belongs to the Special Issue Recent Advances in Polarimetric SAR Interferometry)
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Open AccessArticle Nearest-Regularized Subspace Classification for PolSAR Imagery Using Polarimetric Feature Vector and Spatial Information
Remote Sens. 2017, 9(11), 1114; doi:10.3390/rs9111114
Received: 14 August 2017 / Revised: 19 October 2017 / Accepted: 29 October 2017 / Published: 1 November 2017
PDF Full-text (1356 KB) | HTML Full-text | XML Full-text
Abstract
Feature extraction using polarimetric synthetic aperture radar (PolSAR) images is of great interest in SAR classification, no matter if it is applied in an unsupervised approach or a supervised approach. In the supervised classification framework, a major group of methods is based on
[...] Read more.
Feature extraction using polarimetric synthetic aperture radar (PolSAR) images is of great interest in SAR classification, no matter if it is applied in an unsupervised approach or a supervised approach. In the supervised classification framework, a major group of methods is based on machine learning. Various machine learning methods have been investigated for PolSAR image classification, including neural network (NN), support vector machine (SVM), and so on. Recently, representation-based classifications have gained increasing attention in hyperspectral imagery, such as the newly-proposed sparse-representation classification (SRC) and nearest-regularized subspace (NRS). These classifiers provide excellent performance that is comparable to or even better than the classic SVM for remotely-sensed image processing. However, rare studies have been found to extend this representation-based NRS classification into PolSAR images. By the use of the NRS approach, a polarimetric feature vector-based PolSAR image classification method is proposed in this paper. The polarimetric SAR feature vector is constructed by the components of different target decomposition algorithms for each pixel, including those scattering components of Freeman, Huynen, Krogager, Yamaguchi decomposition, as well as the eigenvalues, eigenvectors and their consequential parameters such as entropy, anisotropy and mean scattering angle. Furthermore, because all these representation-based methods were originally designed to be pixel-wise classifiers, which only consider the separate pixel signature while ignoring the spatial-contextual information, the Markov random field (MRF) model is also introduced in our scheme. MRF can provide a basis for modeling contextual constraints. Two AIRSAR data in the Flevoland area are used to validate the proposed classification scheme. Experimental results demonstrate that the proposed method can reach an accuracy of around 99 % for both AIRSAR data by randomly selecting 300 pixels of each class as the training samples. Under the condition that the training data ratio is more than 4 % , it has better performance than the SVM, SVM-MRF and NRS methods. Full article
(This article belongs to the Special Issue Recent Advances in Polarimetric SAR Interferometry)
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Open AccessArticle Assessment of RISAT-1 and Radarsat-2 for Sea Ice Observations from a Hybrid-Polarity Perspective
Remote Sens. 2017, 9(11), 1088; doi:10.3390/rs9111088
Received: 16 June 2017 / Revised: 18 October 2017 / Accepted: 21 October 2017 / Published: 25 October 2017
Cited by 1 | PDF Full-text (17382 KB) | HTML Full-text | XML Full-text
Abstract
Utilizing several Synthetic Aperture Radar (SAR) missions will provide a data set with higher temporal resolution. It is of great importance to understand the difference between various available sensors and polarization modes and to consider how to homogenize the data sets for a
[...] Read more.
Utilizing several Synthetic Aperture Radar (SAR) missions will provide a data set with higher temporal resolution. It is of great importance to understand the difference between various available sensors and polarization modes and to consider how to homogenize the data sets for a following combined analysis. In this study, a uniform and consistent analysis across different SAR missions is carried out. Three pairs of overlapping hybrid- and full-polarimetric C-band SAR scenes from the Radar Imaging Satellite-1 (RISAT-1) and Radarsat-2 satellites are used. The overlapping Radarsat-2 and RISAT-1 scenes are taken close in time, with a relatively similar incidence angle covering sea ice in the Fram Strait and Northeast Greenland in September 2015. The main objective of this study is to identify the similarities and dissimilarities between a simulated and a real hybrid-polarity (HP) SAR system. The similarities and dissimilarities between the two sensors are evaluated using 13 HP features. The results indicate a similar separability between the sea ice types identified within the real HP system in RISAT-1 and the simulated HP system from Radarsat-2. The HP features that are sensitive to surface scattering and depolarization due to volume scattering showed great potential for separating various sea ice types. A subset of features (the second parameter in the Stokes vector, the ratio between the HP intensity coefficients, and the α s angle) were affected by the non-circularity property of the transmitted wave in the simulated HP system across all the scene pairs. Overall, the best features, showing high separability between various sea ice types and which are invariant to the non-circularity property of the transmitted wave, are the intensity coefficients from the right-hand circular transmit and the linear horizontal receive channel and the right-hand circular on both the transmit and the receive channel, and the first parameter in the Stokes vector. Full article
(This article belongs to the Special Issue Recent Advances in Polarimetric SAR Interferometry)
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Open AccessEditor’s ChoiceArticle 3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities
Remote Sens. 2017, 9(10), 1010; doi:10.3390/rs9101010
Received: 30 June 2017 / Revised: 21 August 2017 / Accepted: 22 September 2017 / Published: 29 September 2017
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Abstract
The rapid expansion of cities increases the need of urban remote sensing for a large scale monitoring. This paper provides greater understanding of how TerraSAR-X (TSX) high-resolution abilities enable to reach the spatial precision required to monitor individual buildings, through the use of
[...] Read more.
The rapid expansion of cities increases the need of urban remote sensing for a large scale monitoring. This paper provides greater understanding of how TerraSAR-X (TSX) high-resolution abilities enable to reach the spatial precision required to monitor individual buildings, through the use of a 4 year temporal stack of 100 images over Paris (France). Three different SAR modes are investigated for this purpose. First a method involving a whole time-series is proposed to measure realistic heights of buildings. Then, we show that the small wavelength of TSX makes the interferometric products very sensitive to the ordinary building-deformation, and that daily deformation can be measured over the entire building with a centimetric accuracy, and without any a priori on the deformation evolution, even when neglecting the impact of the atmosphere. Deformations up to 4 cm were estimated for the Eiffel Tower and up to 1 cm for other lower buildings. These deformations were analyzed and validated with weather and in situ local data. Finally, four TSX polarimetric images were used to investigate geometric and dielectric properties of buildings under the deterministic framework. Despite of the resolution loss of this mode, the possibility to estimate the structural elements of a building orientations and their relative complexity in the spatial organization are demonstrated. Full article
(This article belongs to the Special Issue Recent Advances in Polarimetric SAR Interferometry)
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Open AccessArticle A Modified Dual-Baseline PolInSAR Method for Forest Height Estimation
Remote Sens. 2017, 9(8), 819; doi:10.3390/rs9080819
Received: 26 May 2017 / Revised: 21 July 2017 / Accepted: 4 August 2017 / Published: 9 August 2017
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Abstract
This paper investigates the potentials and limitations of a simple dual-baseline PolInSAR (DBPI) method for forest height inversion. This DBPI method follows the classical three-stage inversion method’s idea used in single baseline PolInSAR (SBPI) inversion, but it avoids the assumption of the smallest
[...] Read more.
This paper investigates the potentials and limitations of a simple dual-baseline PolInSAR (DBPI) method for forest height inversion. This DBPI method follows the classical three-stage inversion method’s idea used in single baseline PolInSAR (SBPI) inversion, but it avoids the assumption of the smallest ground-to-volume amplitude ratio (GVR) by employing an additional baseline to constrain the inversion procedure. In this paper, we present for the first time an assessment of such a method on real PolInSAR data over boreal forest. Additionally, we propose an improvement on the original DBPI method by incorporating the sloped random volume over ground (S-RVoG) model in order to reduce the range terrain slope effect. Therefore, a digital elevation model (DEM) is needed to provide the slope information in the proposed method. Three scenes of P-band airborne PolInSAR data acquired by E-SAR and light detection and ranging (LIDAR) data available in the BioSAR2008 campaign are employed for testing purposes. The performance of the SBPI, DBPI, and modified DBPI methods is compared. The results show that the DBPI method extracts forest heights with an average root mean square error (RMSE) of 4.72 m against LIDAR heights for trees of 18 m height on average. It presents a significant improvement of forest height accuracy over the SBPI method (with a stand-level mean improvement of 42.86%). Concerning the modified DBPI method, it consistently improves the accuracy of forest height inversion over sloped areas. This improvement reaches a stand-level mean of 21.72% improvement (with a mean RMSE of 4.63 m) for slopes greater than 10°. Full article
(This article belongs to the Special Issue Recent Advances in Polarimetric SAR Interferometry)
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Open AccessArticle Determining Rice Growth Stage with X-Band SAR: A Metamodel Based Inversion
Remote Sens. 2017, 9(5), 460; doi:10.3390/rs9050460
Received: 30 March 2017 / Revised: 26 April 2017 / Accepted: 3 May 2017 / Published: 10 May 2017
Cited by 2 | PDF Full-text (9335 KB) | HTML Full-text | XML Full-text
Abstract
Rice crops are important in the global food economy, and new techniques are being implemented for their effective management. These techniques rely mainly on the changes in the phenological cycle, which can be investigated by remote sensing systems. High frequency and high spatial
[...] Read more.
Rice crops are important in the global food economy, and new techniques are being implemented for their effective management. These techniques rely mainly on the changes in the phenological cycle, which can be investigated by remote sensing systems. High frequency and high spatial resolution Synthetic Aperture Radar (SAR) sensors have great potential in all-weather conditions for detecting temporal phenological changes. This study focuses on a novel approach for growth stage determination of rice fields from SAR data using a parameter space search algorithm. The method employs an inversion scheme for a morphology-based electromagnetic backscattering model. Since such a morphology-based model is complicated and computationally expensive, a surrogate metamodel-based inversion algorithm is proposed for the growth stage estimation. The approach is designed to provide estimates of crop morphology and corresponding growth stage from a continuous growth scale. The accuracy of the proposed method is tested with ground measurements from Turkey and Spain using the images acquired by the TerraSAR-X (TSX) sensor during a full growth cycle of rice crops. The analysis shows good agreement for both datasets. The results of the proposed method emphasize the effectiveness of X-band PolSAR data for morphology-based growth stage determination of rice crops. Full article
(This article belongs to the Special Issue Recent Advances in Polarimetric SAR Interferometry)
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Open AccessLetter Non-Cooperative Bistatic SAR Clock Drift Compensation for Tomographic Acquisitions
Remote Sens. 2017, 9(11), 1087; doi:10.3390/rs9111087
Received: 29 June 2017 / Revised: 14 September 2017 / Accepted: 19 October 2017 / Published: 25 October 2017
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Abstract
In the last years, an important amount of research has been headed towards the measurement of above-ground forest biomass with polarimetric Synthetic Aperture Radar (SAR) tomography techniques. This has motivated the proposal of future bistatic SAR missions, like the recent non-cooperative SAOCOM-CS and
[...] Read more.
In the last years, an important amount of research has been headed towards the measurement of above-ground forest biomass with polarimetric Synthetic Aperture Radar (SAR) tomography techniques. This has motivated the proposal of future bistatic SAR missions, like the recent non-cooperative SAOCOM-CS and PARSIFAL from CONAE and ESA. It is well known that the quality of SAR tomography is directly related to the phase accuracy of the interferometer that, in the case of non-cooperative systems, can be particularly affected by the relative drift between onboard clocks. In this letter, we provide insight on the impact of the clock drift error on bistatic interferometry, as well as propose a correction algorithm to compensate its effect. The accuracy of the compensation is tested on simulated acquisitions over volumetric targets, estimating the final impact on tomographic profiles. Full article
(This article belongs to the Special Issue Recent Advances in Polarimetric SAR Interferometry)
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