Beyond the 12m TanDEM-X DEM

The standard TanDEM-X product meats HRTI-3 DEM specification and comes with a sample spacing of 12 m.We apply non-local means (NL) interferogram filtering to the TanDEM-X data. In this paper, we present modifications of the original NL filter which render it more appropriate and efficient for massive processing of TanDEM-X data. Further, we investigate the noise reduction properties as well as the resolution and the coherence estimation accuracy of the new NL filter. Simulations and tests with TanDEM-X data hint that the improved DEMs possess a quality close to the HRTI-4 standard. Also future global InSAR missions like Tandem-L will greatly benefit from this type of filters

Amplitude-Driven-Adaptive-Neighbourhood Filtering of High-Resolution Pol-InSAR Information

International audienceIn this paper a new method for fltering coherency matrices issued from Synthetic Aperture Radar (SAR) polarimetric interferometric data is presented. For each pixel of the interferogram, an adaptive neighborhood is determined by a region growing technique driven exclusively by the amplitude image information. All the available amplitude images of the interferometric couple are fused in the region growing process to ensure the stationarity hypothesis of the derived statistical population. In addition, for preserving local stationarity requirement of the interferogram, a phase compensation step is performed. Afterwards, all the pixels within the obtained adaptive neighborhood are complex averaged to yield the fltered values of the polarimetric and interferometric coherency matrices. The method has been tested on airborne high-resolution polarimetric interferometric SAR images (Oberpfaffenhofen area - German Space Agency). For comparison purposes, the standard phase compensated fixed multi-look flter and the linear adaptive coherence flter proposed by Lee at al. were also implemented. Both subjective and objective performance analysis, including coherence edge detection, ROC graph and bias reduction tables, recommends the proposed algorithm as a powerful post-processing POL-InSAR tool

Coherence maps application for InSAR data accuracy improving

В работе представлен анализ методов применения карт когерентности для обработки интерферометрических пар изображений радиолокаторов с синтезированной апертурой (РСА). Экспериментально определены размеры окон усреднений, допустимых для решения практических задач. Представлен метод повышения точности цифровых моделей рельефа и карт подвижек рельефа, получаемых при интерферометрической съемке, основанный на маскировании карты когерентности. Показано повышение точности результата по сравнению с классической методикой.The paper presents the analysis of coherence maps application methods for the interferometric SAR images processing. The interferometric coherence is an important indicator of the reliability of the interferograms obtained by the interferometric synthetic aperture radar (InSAR), since the areas with low coherence values are unsuitable for processing the interferometric data. In addition, the coherence is used as a parameter of adaptive phase noise filters, and it can also be used for surface segmentation. The sizes of the averaging windows suitable for the solution of practical problems are experimentally determined. The method of accuracy increasing for the digital elevation maps and displacement maps obtained by InSAR systems based on masking the coherence map is presented. The DEM accuracy improvement in comparison with the classical estimation method is presented

Mapping of shifting tidal estuaries to support inshore rescue

Across the world, many coastal tidal regions are unsafe to navigate due to shifting mud and sand pushed by water currents. Ability to regularly map the current location of a channel will aid safe passage for commercial, leisure and rescue craft. This work investigates the use of synthetic aperture radar data derived from satellites to provide accurate mapping of moving channels in coastal regions. As images must be collected at low tide, data availability is assessed considering the relationship between the orbital motion of the satellites and the tides. Change detection methods are applied to suitable images to map changes in the location of navigable channels. Pixels that undergo similar changes over time (e.g. from water covered to exposed sand) are grouped together by examining the principal component of the covariance matrix, for a vector composed of pixel values from the same location at different times. The Solway Firth in Great Britain is selected as a trial site as it is exposed to some of Europe's fastest tidal movements and ranges, and hence is one of Great Britain's most treacherous stretches of coastline

Interferometric SAR deformation timeseries: a quality index

Estimating unknown absolute phase from a wrapped observation is a challenging and ill-posed problem that possibly leads to misinterpretation of interferometric SAR (InSAR) deformation results. In this study, we introduce a quality index to cluster post-phase unwrapping multi-master InSAR timeseries outputs based on the estimated phase residuals and redundancy of network of interferograms. The index is supposed to indicate the reliability of a timeseries, including the identification of persistent scatterers (PSs) possibly affected by phase unwrapping jumps. The algorithm was tested on two Sentinel-1 interferometric datasets with 622,991 and 95,398 PSs, generated from the PSI processing chain PSIG of the geomatics division of CTTC. Promising result have been achieved-especially in identifying erroneous PSs with phase unwrapping jumps. Along with existing temporal phase consistency checking algorithms, the approach could provide rich information toward a better interpretation of the deformation timeseries results.This work has been funded by AGAUR, Generalitat de Catalunya, in the framework of Resolution EMC/ 2459/2019, FI-2020.Peer ReviewedPostprint (published version

A full non-parametric approach for SAR Coherent Change Detection

Synthetic Aperture Radar (SAR) is widely used in heterogeneous fields with aims strictly dependent on the objectives of the application. One of the most common is the exploitation of the Interferometric-SAR (InSAR) to measure millimeter movements on the Earth's surface, aiming to monitor failures or measure infrastructures' health state. In this context, developing algorithms to detect temporal and spatial changes in the radar targets becomes very important. This paper focuses on the temporal change detection framework, proposing a non-parametric Coherent Change Detection (CCD) algorithm called Permutational Change Detection (PCD). The PCD estimates the temporal Change Points (CPs) of a radar target recognizing blocks structure in the coherence matrix without making any assumptions. The performance analysis on simulated data is accomplished, considering a realistic scenario where the geometrical and temporal decorrelation are properly modeled. Finally, the PCD is compared with a parametric CCD algorithm based on the Generalized Likelihood Ratio Test (GLRT)

The comparison of using satellite SAR and optical data in the process of urban growth monitoring.

The aim of this project was to monitor the temporal growth of the urban areas, on the example of the Krakow city (Poland). In recent years more frequent use of satellite data in environmental monitoring can be observed. Definitely the optical data are the most popular type of it.  This kind of data are commonly used in many applications like land cover change detection, biomass study and in the map preparation process. Despite their many advantages they are very sensitive on the weather conditions. Thus they can not be gathered in cloudy or rainy day. This case doesn’t occur when the satellite SAR (Synthetic Aperture Radar) system are used. The ability of SAR and optical systems in monitoring the temporal growth of the urban areas were presented in the past (Opido 2015, Al Rawashdeh 2006). In these projects SAR and optical satellite systems were compered.            The presented here study were performed on fifty archival SAR and optical images acquired between years 1992 and 2010. The images were grouped into five two-year time intervals.  Each interval contain data stack of eight SAR and 2 Landsat images. For each group the analysis of land cover were performed. Each optical image was classified into the three classes: water, urban and green areas. The study of SAR data were based on the analysis of coherent scatterers (Porzycka-Strzelczyk 2015). The most common used methods of coherent scatterer’s identification were tested: dispersion of amplitude, Log-Cumulant (Nicolas 204), Signal-to-cluter ratio (Ulander 2010) and coherency method (Touzi 1999).            The growth of urban area was calculated by studying changes in the number of coherent scatterers on the SAR images. For the Landsat images the changes in area of the urban class were analyzed. Furthermore, the regions of most and least intensive urban growth were detected. The next step of the project is to compare the presented results with new ESA (European Space Agency) satellites. Sentinel-1 provides SAR images with a much better spatial resolution than ERS-1, ERS-2 and Envisat satellites. Sentinel-2 has better spatial resolution and more spectral bands than Landsat-8 (Masek 2015). This will allow to achieve more precise maps of coherent scatterers. 

A Comparison of Sentinel-1 Biased and Unbiased Coherence for Crop Monitoring and Classification

Synthetic Aperture Radar (SAR) holds significant potential for applications in crop monitoring and classification. Interferometric SAR (InSAR) coherence proves effective in monitoring crop growth. Currently, the coherence based on the maximum likelihood estimator is biased towards low coherence values. Therefore, the main aim of this work is to access the performance of Sentinel-1 time-series biased coherence and unbiased coherence in crop monitoring and classification. This study was conducted during the 2018 growing season (April-October) in Komoka, an agricultural region in southwestern Ontario, Canada, primarily cultivating three crops: soybean, corn, and winter wheat. To verify the ability of coherence to monitor crops, a linear correlation coefficient between temporal coherence and dual polarimetric radar vegetation index (DpRVI) was fitted. The results revealed a stable correlation between temporal coherence and DpRVI time-series, with the highest correlation observed for soybean (0.7 < R < 0.8), followed by wheat and corn. Notably, unbiased coherence of the VV channel exhibited the highest correlation (R > 0.75). In addition, we applied unbiased coherence to crop classification. The results show that unbiased coherence exhibits very promising classification performance, with the overall accuracy (84.83%) and kappa coefficient (0.76) of VV improved by 8.35% and 0.12, respectively, over biased coherence, and the overall accuracy (73.25%) and kappa coefficient (0.57) of VH improved by 7.56% and 0.14, respectively, over biased coherence, and all crop classification accuracies were also effectively improved. This study demonstrates the feasibility of coherence monitoring of crops and provides new insights in enhancing the higher separability of crops

A polarimetric target detector using the Huynen fork

The contribution of SAR polarimetry in target detection is described and found to add valuable information. A new target detection methodology is described that makes novel use of the polarization fork of the target. The detector is based on a correlation procedure in the target space, and other target representations (e.g. Huynen parameters or α angle) can be employed. The mathematical formulation is general and can be applied to any kind of single target, however in this paper the detection is optimized for the odd and even-bounces (first two elements of the Pauli scattering vector) and oriented dipoles. Validation against real data shows significant agreement with the expected results based on the theoretical description

Multi-Annual Evaluation of Time Series of Sentinel-1 Interferometric Coherence as a Tool for Crop Monitoring

Interferometric coherence from SAR data is a tool used in a variety of Earth observation applications. In the context of crop monitoring, vegetation indices are commonly used to describe crop dynamics. The most frequently used vegetation indices based on radar data are constructed using the backscattered intensity at different polarimetric channels. As coherence is sensitive to the changes in the scene caused by vegetation and its evolution, it may potentially be used as an alternative tool in this context. The objective of this work is to evaluate the potential of using Sentinel-1 interferometric coherence for this purpose. The study area is an agricultural region in Sevilla, Spain, mainly covered by 18 different crops. Time series of different backscatter-based radar vegetation indices and the coherence amplitude for both VV and VH channels from Sentinel-1 were compared to the NDVI derived from Sentinel-2 imagery for a 5-year period, from 2017 to 2021. The correlations between the series were studied both during and outside the growing season of the crops. Additionally, the use of the ratio of the two coherences measured at both polarimetric channels was explored. The results show that the coherence is generally well correlated with the NDVI across all seasons. The ratio between coherences at each channel is a potential alternative to the separate channels when the analysis is not restricted to the growing season of the crop, as its year-long temporal evolution more closely resembles that of the NDVI. Coherence and backscatter can be used as complementary sources of information, as backscatter-based indices describe the evolution of certain crops better than coherence.This research work was supported by the the European Space Agency under Project SEOM-S14SCI-Land (SInCohMap), and by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development (Project PID2020-117303GB-C22)