Range Spectral Filtering in SAR Interferometry: Methods and Limitations

A geometrical decorrelation constitutes one of the sources of noise present in Synthetic Aperture Radar (SAR) interferograms. It comes from the different incidence angles of the two images used to form the interferograms, which cause a spectral (frequency) shift between them. A geometrical decorrelation must be compensated by a specific filtering technique known as range filtering, the goal of which is to estimate this spectral displacement and retain only the common parts of the images’ spectra, reducing the noise and improving the quality of the interferograms. Multiple range filters have been proposed in the literature. The most widely used methods are an adaptive filter approach, which estimates the spectral shift directly from the data; a method based on orbital information, which assumes a constant-slope (or flat) terrain; and slope-adaptive algorithms, which consider both orbital information and auxiliary topographic data. Their advantages and limitations are analyzed in this manuscript and, additionally, a new, more refined approach is proposed. Its goal is to enhance the filtering process by automatically adapting the filter to all types of surface variations using a multi-scale strategy. A pair of RADARSAT-2 images that mapped the mountainous area around the Etna volcano (Italy) are used for the study. The results show that filtering accuracy is improved with the new method including the steepest areas and vegetation-covered regions in which the performance of the original methods is limited.This work was supported by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development (ERFD) under Projects PID2020-117303GB-C21 and PID2020-117303-C22

An Improved Phase Filter for Differential SAR Interferometry Based on an Iterative Method

Phase quality is a key element in the analysis of the deformation of the Earth's surface carried out with differential synthetic aperture radar interferometry. Various decorrelation sources may degrade the surface deformation estimates, and thus, phase filters are needed for this kind of application. The well-known Goldstein filter is the most widely used due to its simple implementation and computational efficiency. In the past years, improved filters have been proposed, which are based on this filter but introduce variations in the data processing. The effectiveness of these filters mostly depends on the size of the filtering window, the weight of the smoothed spectrum, and the kernel used to filter the spectrum. In this paper, we evaluate the performance of four of these filters and present a new method that outperforms all of them. The proposed filter is based on an iterative method in which the original phase is denoised progressively with adaptive filtering windows of different sizes. The effectiveness of the filter is controlled by the interferometric coherence, a direct indicator of the phase quality. Moreover, we introduce some modifications regarding the processing of the power spectrum. Specifically, we propose to smooth the original phase using a new filter which is based on a Chebyshev interpolation scheme. The performance of the new filter has been tested on both simulated and real interferograms, acquired by RADARSAT-2 and the Uninhabited Aerial Vehicle Synthetic Aperture Radar, which mapped two different geological events that caused surface deformation.This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness, in part by the State Agency of Research (AEI), in part by the European Funds for Regional Development under Project TIN2014-55413-C2-2-P and Project TEC2017-85244-C2-1-P, in part by the U.K. Natural Environmental Research Council through the Looking Inside the Continents under Grant NE/K011006/1, in part by the Rapid deployment of a seismic array in Ecuador following the April 16th 2016 M7.8 Pedernales earthquake under Grant NE/P008828/1, and in part by the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics under Grant COMET, GA/13/M/031

Time Series of Sentinel-1 Interferometric Coherence and Backscatter for Crop-Type Mapping

The potential use of the interferometric coherence measured with Sentinel-1 satellites as input feature for crop classification is explored in this study. A one-year time series of Sentinel-1 images acquired over an agricultural area in Spain, in which 17 crop species are present, is exploited for this purpose. Different options regarding temporal baselines, polarisation, and combination with radiometric data (backscattering coefficient) are analysed. Results show that both radiometric and interferometric features provide notable classification accuracy when used individually (overall accuracy lies between 70% and 80%). It is found that the shortest temporal baseline coherences (6 days) and the use of all available intensity images perform best, hence proving the advantage of the 6-day revisit time provided by the Sentinel-1 constellation with respect to longer revisit times. It is also shown that dual-pol data always provide better classification results than single-pol ones. More importantly, when both coherence and backscattering coefficient are jointly used, a significant increase of accuracy is obtained (greater than 7% in overall accuracy). Individual accuracies of all crop types are increased, and an overall accuracy above 86% is reached. This proves that both features provide complementary information, and that the combination of interferometric and radiometric radar data constitute a solid information source for this application.This work was supported in part by the European Space Agency via the ESA SEOM Program ITT under Grant AO/1-8306/15/I-NB “SEOM-S14SCI Land,” and in part by the Spanish Ministry of Science, Innovation and Universities, the State Agency of Research (AEI), and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P

Exploring TanDEM-X Interferometric Products for Crop-Type Mapping

The application of satellite single-pass interferometric data to crop-type mapping is demonstrated for the first time in this work. A set of nine TanDEM-X dual-pol pairs of images acquired during its science phase, from June to August 2015, is exploited for this purpose. An agricultural site located in Sevilla (Spain), composed of fields of 13 different crop species, is employed for validation. Sets of input features formed by polarimetric and interferometric observables are tested for crop classification, including single-pass coherence and repeat-pass coherence formed by consecutive images. The backscattering coefficient at HH and VV channels and the correlation between channels form the set of polarimetric features employed as a reference set upon which the added value of interferometric coherence is evaluated. The inclusion of single-pass coherence as feature improves by 2% the overall accuracy (OA) with respect to the reference case, reaching 92%. More importantly, in single-pol configurations OA increases by 10% for the HH channel and by 8% for the VV channel, reaching 87% and 88%, respectively. Repeat-pass coherence also improves the classification performance, but with final scores slightly worse than with single-pass coherence. However, it improves the individual performance of the backscattering coefficient by 6–7%. Furthermore, in products evaluated at field level the dual-pol repeat-pass coherence features provide the same score as single-pass coherence features (overall accuracy above 94%). Consequently, the contribution of interferometry, both single-pass and repeat-pass, to crop-type mapping is proved.This work was funded by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P, and by the European Commission, H2020 Programme, under Project MOSES (Managing crOp water Saving with Enterprise Services)

Iterative Filtering Based on Adaptive Chebyshev Kernel Functions for Noise Supression in Differential SAR Interferograms

Differential SAR Interferometry (DInSAR) is a powerful remote sensing technique employed to monitor surface displacements, such as ground subsidence or strong deformations caused by geological activity. The quality of the interferometric phase between two combined SAR images is essential for the estimation of the surface deformation. Multi-pIe decorrelation factors may degrade the quality of the measurements and, then, the development of filtering methods for noise suppression is mandatory. In this work, we propose a new strategy to improve noise reduction while preserving the original phase structure. The new method consists in an iterative filter in which noise reduction is achieved progressively. The original phase is filtered with adaptive kernels based on Chebyshev interpolation functions. The filter is especially useful for DInSAR geophysical applications, such as earthquakes or volcanic eruptions monitoring. The performance of the proposed method has been tested with both simulated data and recently acquired Sentinel-1 SAR data which mapped the August 2016 Central Italy earthquake.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the State Agency of Research (AEI) and the European Funds for Regional Development (FEDER) under Projects TIN2014-55413-C2-2-P and TEC2017-85244-C2-1-P. This work was partially supported by the UK Natural Environmental Research Council (NERC) through the “Looking Inside the Continents (LiCS)” (NE/K011006/1), the “Rapid deployment of a seismic array in Ecuador following the April 16th 2016 M7.8 Pedernales earthquake” (NE/P008828/1), and the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET, GA/13/M/031, http://comet.nerc.ac.uk) projects

Advanced Processing Techniques and Applications of Synthetic Aperture Radar Interferometry

Synthetic Aperture Radar interferometry (InSAR) is a powerful and established technique, which is based on exploiting the phase difference between pairs of SAR images, and which aims to measure changes in the Earth’s surface. The quality of the interferometric phase is therefore the most crucial factor for deriving reliable products by means of this technique. Unfortunately, the quality of the phase is often degraded due to multiple decorrelation factors, such as the geometrical or temporal decorrelation. Accordingly, central to this PhD thesis is the development of advanced processing techniques and algorithms to extensively reduce such disturbing effects caused by decorrelation. These new techniques include an improved range spectral filter which fully utilizes an external Digital Elevation Model (DEM) to reduce geometrical decorrelation between pairs of SAR images, especially in areas strongly influenced by topography where conventional methods are limited; an improved filter for the final interferometric phase the goal of which is to remove any remaining noise (for instance, noise caused by temporal decorrelation) while, simultaneously, phase details are appropriately preserved; and polarimetric optimization algorithms which also try to enhance the quality of the phase by exploring all the polarization diversity. Moreover, the exploitation of InSAR data for crop type mapping has also been evaluated in this thesis. Specifically, we have tested if the multitemporal interferometric coherence is a valuable feature which can be used as input to a machine learning algorithm to generate thematic maps of crop types. We have shown that InSAR data are sensitive to the temporal evolution of crops, and, hence, they constitute an alternative or a complement to conventional radiometric, SAR-based, classifications

Range Spectral Filtering in SAR Interferometry: Methods and Limitations

A geometrical decorrelation constitutes one of the sources of noise present in Synthetic Aperture Radar (SAR) interferograms. It comes from the different incidence angles of the two images used to form the interferograms, which cause a spectral (frequency) shift between them. A geometrical decorrelation must be compensated by a specific filtering technique known as range filtering, the goal of which is to estimate this spectral displacement and retain only the common parts of the images’ spectra, reducing the noise and improving the quality of the interferograms. Multiple range filters have been proposed in the literature. The most widely used methods are an adaptive filter approach, which estimates the spectral shift directly from the data; a method based on orbital information, which assumes a constant-slope (or flat) terrain; and slope-adaptive algorithms, which consider both orbital information and auxiliary topographic data. Their advantages and limitations are analyzed in this manuscript and, additionally, a new, more refined approach is proposed. Its goal is to enhance the filtering process by automatically adapting the filter to all types of surface variations using a multi-scale strategy. A pair of RADARSAT-2 images that mapped the mountainous area around the Etna volcano (Italy) are used for the study. The results show that filtering accuracy is improved with the new method including the steepest areas and vegetation-covered regions in which the performance of the original methods is limited

Advanced Processing Techniques and Applications of Synthetic Aperture Radar Interferometry

Synthetic Aperture Radar interferometry (InSAR) is a powerful and established technique, which is based on exploiting the phase difference between pairs of SAR images, and which aims to measure changes in the Earth’s surface. The quality of the interferometric phase is therefore the most crucial factor for deriving reliable products by means of this technique. Unfortunately, the quality of the phase is often degraded due to multiple decorrelation factors, such as the geometrical or temporal decorrelation. Accordingly, central to this PhD thesis is the development of advanced processing techniques and algorithms to extensively reduce such disturbing effects caused by decorrelation. These new techniques include an improved range spectral filter which fully utilizes an external Digital Elevation Model (DEM) to reduce geometrical decorrelation between pairs of SAR images, especially in areas strongly influenced by topography where conventional methods are limited; an improved filter for the final interferometric phase the goal of which is to remove any remaining noise (for instance, noise caused by temporal decorrelation) while, simultaneously, phase details are appropriately preserved; and polarimetric optimization algorithms which also try to enhance the quality of the phase by exploring all the polarization diversity. Moreover, the exploitation of InSAR data for crop type mapping has also been evaluated in this thesis. Specifically, we have tested if the multitemporal interferometric coherence is a valuable feature which can be used as input to a machine learning algorithm to generate thematic maps of crop types. We have shown that InSAR data are sensitive to the temporal evolution of crops, and, hence, they constitute an alternative or a complement to conventional radiometric, SAR-based, classifications

Initial Tests for the Generation of a Spanish National Map of Forest Height from Tandem-X Data

The first results of a project aimed at estimating forest height over the entire Spain by means of TanDEM-X data are shown and discussed in this work. Four test sites representative of the Spanish forests are introduced, as well as the data used for validation of results. Results obtained over one of the test sites (in Teruel province) are presented here. Among the challenges found in the project, the influence of slope in mountain areas and the relatively short height of the forest canopy make this work distinctive to previous projects employing TanDEM-X data for estimation of heights in tropical, boreal and temperate regions.This work was supported by the Spanish Ministry of Science, Innovation and Universities, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P, and by the Natural National Science Foundation of China under Grant nr. 41820104005. Noelia Romero-Puig received a grant from the Gen-eralitat Valenciana and the European Social Fund (ESF) [ACIF/2018/204].Peer reviewe

Polarimetric optimisation methods applied to single differential SAR interferograms for geophysical events

Con el fin de conseguir valores más fiables y continuos de fase diferencial, para así facilitar la posterior interpretación de un suceso geológico en base a los modelos geofísicos disponibles, se han probado diferentes métodos de optimización basados en polarimetría para pares interferométricos de imágenes SAR. Se han empleado datos adquiridos en polarización completa mediante el satélite RADARSAT-2 y que se corresponden con la erupción volcánica del Monte Etna (Italia) en 2008. La optimización polarimétrica se ha llevado a cabo mediante una combinación de los canales polarimétricos disponibles, de modo que se explora el espacio polarimétrico para mejorar el producto final.Attending to a goal consisting in obtaining more reliable and continuous phase measurements, in order to ease the subsequent interpretation and analysis of a geological event with geophysical models, different polarimetric optimisation methods have been tested over single-pair differential SAR interferograms. Tests were carried out on data generated with two quad-polarimetric RADARSAT-2 images covering the volcanic eruption of Mount Etna (Italy) in 2008. Polarimetric optimisation has been carried out by means of a combination of the available polarimetric channels, so the available polarimetric space is searched to improve the final product.Este trabajo fue iniciado durante una estancia de Juan M. López Sánchez en la Universidad de Leeds financiada por el Ministerio de Educación (ref. PRX14/00151) y ha sido financiado por el Ministerio de Economía y Competitividad y los Fondos EU FEDER como parte del proyecto TIN2014-55413-C2-2-P