Interferometric SAR Phase Denoising Using Proximity-Based K-SVD Technique

P>Sea ice monitoring is an important field of scientific research and relevant to operational applications. One of the major engineering challenges in undertaking production developments in Arctic offshore regions is the frequent presence of extreme ice features that pose a hazard to facilities and surrounding subsea infrastructure. The information on extreme ice features (i.e., ridges, icebergs etc.) is important from the standpoint of potential ice load levels on fixed structures and ice scouring of seafloor facilities. Satellite observation has been shown to be useful for extracting and characterizing ice regimes. Sea ice can be monitored using satellite imagery acquired by different types of sensors: microwave radiometer, optical instrument and synthetic aperture radar (SAR). The outputs of sea ice monitoring may include various ice parameters such as edge, thickness, concentration, classification, iceberg detection, and ice statistics. This paper describes application of high and low resolution SAR imagery for sea ice monitoring and to resolve local features and extend the statistical baseline to larger regions because extreme ice features may be invisible or ambiguous with other ice features in these data. The use of higher resolution imagery allows for easier detection of ice features and provides sufficient spatial detail necessary for detecting ice features from sea ice, identification and estimation of size and geometry of ice floes and icebergs. It was demonstrated that SAR sensors with multiple resolution lead to a better understanding of ice conditions including ice edge, concentration, floe statistics, and other ice features such as icebergs. A technique based on SAR interferometry was used for identification of iceberg in sea ice as well as for extracting iceberg topography.

This paper addresses the problem of interferometric phase noise reduction in synthetic aperture radar interferometry. An anisotropic Gaussian filter is proposed in the paper. Different from those conventional isotropic filters, the filter can filter the phase noise according to the local fringe rates and the local noise levels. By using both, simulated and real interferometric phase images, the performance of the filter, in terms of fringes preservation and noise reduction, is demonstrated and compared with the mean filter and the median filter

Oil spill is considered one of the main threats to marine and coastal environments. Efficient monitoring and early identification of oil slicks are vital for the corresponding authorities to react expediently, confine the environmental pollution and avoid further damage. Synthetic aperture radar (SAR) sensors are commonly used for this objective due to their capability for operating efficiently regardless of the weather and illumination conditions. Black spots probably related to oil spills can be clearly captured by SAR sensors, yet their discrimination from look-alikes poses a challenging objective. A variety of different methods have been proposed to automatically detect and classify these dark spots. Most of them employ custom-made datasets posing results as non-comparable. Moreover, in most cases, a single label is assigned to the entire SAR image resulting in a difficulties when manipulating complex scenarios or extracting further information from the depicted content. To overcome these limitations, semantic segmentation with deep convolutional neural networks (DCNNs) is proposed as an efficient approach. Moreover, a publicly available SAR image dataset is introduced, aiming to consist a benchmark for future oil spill detection methods. The presented dataset is employed to review the performance of well-known DCNN segmentation models in the specific task. DeepLabv3+ presented the best performance, in terms of test set accuracy and related inference time. Furthermore, the complex nature of the specific problem, especially due to the challenging task of discriminating oil spills and look-alikes is discussed and illustrated, utilizing the introduced dataset. Results imply that DCNN segmentation models, trained and evaluated on the provided dataset, can be utilized to implement efficient oil spill detectors. Current work is expected to contribute significantly to the future research activity regarding oil spill identification and SAR image processing.

This paper investigates the potential of accounting for temporal contextual information in order to improve the accuracy of land-cover classification in summer with Synthetic Aperture Radar (SAR) data. Bi-temporal multi-sensor datasets collected in the Nonsan area of Korea were used to illustrate this approach. Multi-sensor data, including Japanese Earth Resources Satellite (JERS)-1 Optical Sensor (OPS) data acquired in April, and three different SAR sensor datasets from European Resource Satellite (ERS)-2, JERS-1, and Radarsat-1 obtained in the following July, were used for supervised classification in July. By comparing the classification result in April with a training set in July, transition probabilities between land-cover classes in the April–July period were empirically estimated and regarded as the temporal contextual information. A tau model is applied as a main integration methodology to combine multiple SAR data and the temporal contextual information. From the evaluation of the classification results in terms of accuracy statistics, using multiple SAR sensor data showed an increase of about 29% in overall accuracy compared with the case of single SAR sensor data. The incorporation of temporal contextual information into scattering information greatly contributed to a significant improvement of about 25% in overall accuracy over multiple SAR sensor integration only, and showed the best discrimination capability.

Coal mine subsidence hazard can be effectively evaluated by geographic information system (GIS) analysis if sufficient data is provided. It is, however, difficult to obtain ground-based data, especially in remote and less populated mining areas. In this study, we construct and validate a coal mine subsidence hazard map in Taebaek, Korea, by integration of space-borne L-band synthetic aperture radar (SAR) measurements and a fuzzy-based subsidence model. There is an approximately 15-year time interval between the radar measurements used for the subsidence hazard model and those used to validate the subsidence. A subsidence hazard map was constructed using Japanese Earth Resources Satellite (JERS-1) SAR data from the early 1990s and the subsidence model. For the coal mine subsidence hazard mapping, a certainty factor analysis was used to estimate the relative weights of four control factors influencing coal mine subsidence, and the relative weight of each factor was then integrated to produce a subsidence hazard index by a fuzzy combination operator. The predicted hazard areas were then investigated and validated by comparison with subsidence occurrences observed by Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) interferometry in 2007–2008. The results showed a good agreement between the predicted locations vulnerable to subsidence and the actual subsidence occurrences with a prediction accuracy of about 73% and a root mean square error of about 0.35. These results demonstrate that a map produced by integration of a subsidence model and SAR interferometry can be used to predict and monitor coal mine subsidence hazards, especially in remote regions.

Synthetic Aperture Radar (SAR) interferometry is an active remote sensing technology that uses microwaves to characterize the earth's surface. SAR interferometry allows to measure the 3D profile of the earth's surface, recover surface topography, and determine topographic displacements over time. The microwave SAR signal is usually highly distorted. Distortions can be caused by, for example, atmospheric disturbances and various characteristics of earth's surface scatterers reflectance. Compensation for these distortions is performed by filtering the phase and evaluating the degree of coherence of the original images. This is an important step to improve the accuracy of the subsequent pphase-unwrapping operation. In this paper, we investigate the use of U-net neural networks for preprocessing the SAR interferogram at various parameters of the distortion of the SAR signal. Two neural networks filter the SAR interferogram and determine the degree of coherence, respectively.

Anthropogenic land subsidence is an example of changes to the natural environment due to human activities and is one of the key factors in causing land degradation at a range of scales. Previous studies assessing land subsidence in the Valley of Mexico either focused on regional scale or short (noncontinuous) temporal scale. In this study, long‐term land subsidence (~15 years) is mapped in Mexico City (Mexico) using two interferometric synthetic aperture radar (InSAR) methods, namely, GEOS (Geoscience and Earth Observing Systems Group)‐Advance Time‐series Analysis and GEOS‐Small Baseline Subset. An inverse distance weighted‐based integration module and maximum likelihood regression‐based M estimator are introduced to further enhance these two methods. The land subsidence was continuously mapped using ENVISAT (2004–2007), ALOS‐1 (2007–2011), COSMO‐SkyMed (2011–2014), ALOS‐2 (2014–2018), and SENTINEL‐1 (2015–2017) data sets. A comparison between InSAR time‐series and GPS measurement shows that the subsidence rates are consistent over 2004–2018. The subsidence map over 15 years was generated finding a maximum subsidence over 4.5 m. By comparing our InSAR results with a land use map, we find that the subsidence centre in Mexico City is mostly located in the residential regions with the consumption of groundwater contributing considerably to the local subsidence rate. A total volume of 1.20 × 108 m3 of the land in Ciudad Nezahualcoyotl subsided/degraded. A continuing subsidence process limits the potential land use causing serious land degradation. Our results may be used to assist disaster reduction plans.

Nowadays, image compression is very important for efficient data storage and transmission. This paper presents a dictionary-based intra-prediction framework for image compression using sparse representation, with the construction of trained over-complete dictionaries. The intra-prediction residuals selected from different images and K-SVD algorithm are used to train over-complete dictionaries. The trained dictionaries are integrated into the intra-prediction framework for efficient image compression. In this proposed method, first, intra-prediction is applied over an image and then prediction residuals of the image are encoded using sparse representation. Sparse approximation algorithm and trained dictionaries are employed for encoding of prediction residuals of the image. The coefficients obtained from sparse representation are used for encoding. For efficient sparse representation with fewer dictionary coefficients, an adaptive sparse image partitioning method is introduced. Simulation result demonstrates that the proposed image compression method yields improved encoding efficiency as compared to existing schemes.

Excessive groundwater exploitation in Sirjan plain, located in south of Iran, has caused severe land subsidence. The main purpose of this paper is to determine the hydrogeological parameters of the aquifer system in Sirjan plain. To this end, the groundwater flow was simulated in both steady and transient states using the United States Geological Survey Modular Three-Dimensional Finite-Difference Groundwater Flow Model, MODFLOW. The hydraulic conductivity parameter of the aquifer system was first estimated and calibrated in the steady state by simulating the hydraulic head at 63 piezometric wells. The specific yield and recharge parameters were then calibrated in the transient state by simulating the drawdown within a period of interest, i.e. 23 September 2004 to 22 September 2005. The main innovation of this paper, however, is to use the potential of Synthetic Aperture Radar (SAR) interferometry time-series analysis in the last step of the proposed algorithm. Small Baseline Subset (SBAS) time-series analysis was applied using Envisat Advanced SAR (ASAR) images acquired from 1 June 2004 to 28 September 2010. The merit of SBAS time series is that it enables us to calculate the amount of subsidence in each period of interest while minimizing the temporal decorrelation effect. In the last step, the SBAS time-series analysis results were obtained to extract the accumulated subsidence from 19 October 2004 to 30 August 2005. The aquifer compaction due to this period was calculated in the Interbed Storage Package (IBS1) for MODFLOW in order to calibrate storage coefficient in a trial-and-error manner so as the calculated subsidence and the observed one by SAR interferometry be in a good agreement. The Root Mean Square Error (RMSE) between the simulated subsidence and the observed one is calculated as 0.019 m which is an indication of the high performance of modelling.

Abstract. Floods are extreme hydrological events that can reshape the landscape, transform entire ecosystems and alter the relationship between living organisms and the surrounding environment. Every year, fluvial and coastal floods claim thousands of human lives and cause enormous direct damages and inestimable indirect losses, particularly in less developed and more vulnerable regions. Monitoring the spatiotemporal evolution of floods is fundamental to reducing their devastating consequences. Observing floods from space can make the difference: from this distant vantage point it is possible to monitor vast areas consistently, and, by leveraging multiple sensors on different satellites, it is possible to acquire a comprehensive overview on the evolution of floods at a large scale. Synthetic aperture radar (SAR) sensors, in particular, have proven extremely effective for flood monitoring, as they can operate day and night and in all weather conditions, with a highly discriminatory power. On the other hand, SAR sensors are unable to reliably detect water in some cases, the most critical being urban areas. Furthermore, flood water depth – which is a fundamental variable for emergency response and impact calculations – cannot be estimated remotely. In order to address such limitations, this study proposes a framework for estimating flood water depths and enhancing flood delineations, based on readily available topographical data. The methodology is specifically designed to accommodate, as additional inputs, masks delineating water bodies and/or no-data areas. In particular, the method relies on simple morphological arguments to expand flooded areas into no-data regions and to estimate water depths based on the terrain elevation of the boundaries between flooded and non-flooded areas. The underlying algorithm – named FLEXTH – is provided as Python code and is designed to run in an unsupervised mode in a reasonable time over areas of several hundred thousand square kilometers. This new tool aims to quantify and ultimately to reduce the impacts of floods, especially when used in synergy with the recently released Global Flood Monitoring product of the Copernicus Emergency Management Service.

The paper deals the presence of a natural source of oil, which is sometimes detected in the southeastern part of the Black Sea as part of the monitoring of oil pollution of the Black Sea using the EMSA (European Maritime Safety Agency) satellite service. With the collaboration of EMSA CLEANSEANET System and MRCC (Maritime Rescue Coordination Center) of Maritime Agency of Georgia MRCC is regularly receiving data to region possible spills pollution. The EMSA satellite service offers extensive surveillance of European waters for oil spills by using radar images acquired by Synthetic Aperture Radar (SAR) sensors on polar orbiting satellites. SAR sensors have the capability to detect oil slicks on the sea surface in darkness as well as daylight hours and to see through clouds. The analysis CleanSeaNet Alert Report possible spills, it's safe to say that we are observing a natural expression of oil on the sea surface, which is formed by the migration of oil hydrocarbons in the sedimentary complex of the southeastern part of the Black Sea, seaward of the city of Poti (Georgia). Source identified based on the analysis of multi-temporal radar satellite imagery.

Synthetic Aperture Radar (SAR) is a remote sensing technique that allows the generation of microwave images of the Earth surface, independently of weather condition and of sun illumination. SAR sensors can be either mounted on spacecrafts (spaceborne systems) or placed on airplanes (airborne systems). In this thesis the study of problems related to the use of airborne SAR is addressed. The thesis is organized in five chapters and Chapter 1 presents the basic rationale of SAR technique. The assumption that the sensor flight path is a straight line is adopted in the first chapter, which is preliminary to the subsequent analysis, and allows introducing the formalism adopted in Chapters 2-5, where the assumption of straight flight path, not realistic for airborne systems, is relaxed, and the original results of this thesis are presented. Airborne SAR acquisition model is deeply analyzed, and realization of an efficient airborne raw data simulator, which represents a useful tool for mission planning, SAR system design and inversion algorithm testing, is presented (Chapter 2). The analysis of the airborne acquisition model allows also highlighting problems related to airborne SAR focusing. In particular, a quantitative analysis of the accuracy achievable by airborne SAR images is performed; extension to the repeat pass airborne interferometry is also included (Chapter 3). Such an analysis allows, on one side, quantifying the airborne repeat pass interferogram phase accuracy in terms of the expected motion measurement system accuracy (Chapter 3); on the other side, designing a new efficient airborne SAR processor (Chapter 4). Theoretical results presented in Chapter 3 are, finally, validated by an airborne differential interferometry experiment carried out by using real data (Chapter 5).

Synthetic aperture radar (SAR) has been an unparalleled tool in cloudy and rainy regions as it allows observations throughout the year because of its all-weather, all-day operation capability. In this paper, the influence of Wenchuan Earthquake on the Sichuan Giant Panda habitats was evaluated for the first time using SAR interferometry and combining data from C-band Envisat ASAR and L-band ALOS PALSAR data. Coherence analysis based on the zero-point shifting indicated that the deforestation process was significant, particularly in habitats along the Min River approaching the epicenter after the natural disaster, and as interpreted by the vegetation deterioration from landslides, avalanches and debris flows. Experiments demonstrated that C-band Envisat ASAR data were sensitive to vegetation, resulting in an underestimation of deforestation; in contrast, L-band PALSAR data were capable of evaluating the deforestation process owing to a better penetration and the significant coherence gain on damaged forest areas. The percentage of damaged forest estimated by PALSAR decreased from 20.66% to 17.34% during 2009–2010, implying an approximate 3% recovery rate of forests in the earthquake impacted areas. This study proves that long-wavelength SAR interferometry is promising for rapid assessment of disaster-induced deforestation, particularly in regions where the optical acquisition is constrained.

Online Material: Additional figures and tables. On 20 May 2012, an M L 5.9 earthquake (Table 1) occurred near the town of Finale Emilia, in the central Po plain, Northern Italy (Fig. 1a). The mainshock caused seven casualties and the collapse of several historical buildings and industrial sheds. The earthquake sequence continued with diminishing aftershock magnitudes until 29 May, when an M L 5.8 earthquake occurred near the town of Mirandola, ∼12 km west‐southwest of the mainshock (Scognamiglio et al. , 2012). This second mainshock started a new aftershock sequence in this area, and increased structural damage and collapses, causing 19 more casualties and increasing to 15,000 the number of evacuees. View this table: Table 1 Largest Earthquakes ( M L>5) of the 2012 Emilia Seismic Sequence (http://iside.rm.ingv.it) Figure 1. (a) Geostructural setting of the Emilia 2012 seismic sequence area showing the vertically projected top traces of the main thrust fault planes, and the anticlinal axes, modified after (Bigi et al. , 1983; Boccaletti et al. , 2010; Picotti and Pazzaglia, 2008); white dots, aftershock epicenters. Large instrumental and historical earthquakes are also shown (http://iside.rm.ingv.it, last accessed April 2013); Rovida et al. , 2011); yellow rectangles, footprint of the COSMO‐SkyMed (CSK) and Radarsat satellite image pairs. The focal mechanisms of the two main events (from Malagnini et al. , 2012) are reported in the map. (b) Geological profiles interpreted from seismic reflection data after (Boccaletti et al. , 2010) corresponding to traces A and B in (a). Shortly after the first mainshock, the Department of Civil Protection (DPC) activated the Italian Space Agency (ASI), which provided post‐seismic Synthetic Aperture Radar (SAR) Interferometry data coverage with all four Constellation of Small Satellites for the Mediterranean Basin Observation (COSMO‐SkyMed) SAR satellites. Within the next two weeks, several SAR Interferometry (InSAR) image pairs were processed by the Istituto Nazionale di Geofisica e Vulcanologia–Space‐based Monitoring System …

Although Synthetic Aperture Radar (SAR) can capture rich land cover information as a most important advanced technique in the field of international earth observation, the application effects still limited significantly. The reason is that the study of SAR imaging processing, SAR image processing and SAR applications are conducted respectively, the integrated study are lacked for parameter selection of SAR imaging processing and SAR image processing and targets identification of SAR application. Focusing on above science problems, the study for phase high fidelity model of high precision interferometric SAR, SAR non-stationary backscattering characteristics and cognition, SAR three-dimensional electromagnetic scattering modeling and its scattering mechanism and SAR multi-parameters optimization information extraction has been conducted. The typical natural distributed targets and man-made targets such as surface deformation, sea ice, building complex and collapsed building are selected, the method and application study oriented to SAR environmental parameters inversion are conduced, the main result and conclusion are as follows: (1) The overall theoretical frames of SAR information integrated processing was proposed; The simulation and ground validation system used to assess the effects of SAR information integrated processing was created; furthermore, the spaceborne SAR imaging algorithm with three steps focusing processing was proposed and realized the multi-modal integrated imaging processing and improved the imaging processing precision. (2) In terms of SAR information integrated processing of natural targets, focusing on phase high fidelity imaging processing of interferometric SAR surface deformation monitoring, an novel spaceborne SAR high fidelity simulation method based on stationary RCS and improved Goldstein SAR interferogram filter based on empirical mode decomposition were proposed. Focusing on multi-parameters optimization processing of sea ice types classification, the multi-channel spaceborne SAR sparse imaging method based on compressive sensing, Kalman filter for removal of scalloping and inter-scan banding in ScanSAR images and SVM sea ice classification method combined with sea ice concentration were proposed. (3) In terms of SAR information integrated processing of man-made targets, focusing on non-stationary backscattering from building complex, mitigation of azimuth ambiguities in spaceborne stripmap SAR iamges using selective restoration, SAR image despeckling by selective 3D filtering of multiple compressive reconstructed images and man-made target detection in urban areas based on a new azimuth stationary extraction method were proposed. Focusing on three-dimensional modeling and fast extraction of collapsed building, H-a-p method used for collapsed building extraction was proposed.