136 research outputs found

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Conceptual Study and Performance Analysis of Tandem Dual-Antenna Spaceborne SAR Interferometry

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    Multi-baseline synthetic aperture radar interferometry (MB-InSAR), capable of mapping 3D surface model with high precision, is able to overcome the ill-posed problem in the single-baseline InSAR by use of the baseline diversity. Single pass MB acquisition with the advantages of high coherence and simple phase components has a more practical capability in 3D reconstruction than conventional repeat-pass MB acquisition. Using an asymptotic 3D phase unwrapping (PU), it is possible to get a reliable 3D reconstruction using very sparse acquisitions but the interferograms should follow the optimal baseline design. However, current spaceborne SAR system doesn't satisfy this principle, inducing more difficulties in practical application. In this article, a new concept of Tandem Dual-Antenna SAR Interferometry (TDA-InSAR) system for single-pass reliable 3D surface mapping using the asymptotic 3D PU is proposed. Its optimal MB acquisition is analyzed to achieve both good relative height precision and flexible baseline design. Two indicators, i.e., expected relative height precision and successful phase unwrapping rate, are selected to optimize the system parameters and evaluate the performance of various baseline configurations. Additionally, simulation-based demonstrations are conducted to evaluate the performance in typical scenarios and investigate the impact of various error sources. The results indicate that the proposed TDA-InSAR is able to get the specified MB acquisition for the asymptotic 3D PU, which offers a feasible solution for single-pass 3D SAR imaging.Comment: 16 pages, 20 figure

    Land Surface Monitoring Based on Satellite Imagery

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    This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

    Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

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    With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

    An investigation of ongoing displacements of active faults in the Gobi desert using persistent scatterer interferometric synthetic aperture radar technique to support the permanent disposal of high-level waste in Beishan, China

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    This research demonstrated the application of PSInSAR method in identifying and characterising the micro-displacements along active faults in Beishan to support the selection of GDF host rock. This research first distinguishes and separates the tectonic induced and non-tectonic induced deformation within three study areas at Suanjingzi, Jiujing and Xinchang. Through the application of coherence change detection, it found the granite outcrop areas characterised by high coherence provide more robust results of tectonic activity. The Quaternary sediments covered areas which are characterised by low coherence usually show higher deformation rates due to the impacts of erosion and deposition. The tectonic induced displacements generally range from -0.4 to 0.4 mma-1 and are dominated by fault bound tectonic movements. As a part of wrench faut zone, Beishan is impacted by a NE-SW trended maximum in situ compressive stress field (σ1). To correlate the visible valleys, gullies, or cracks in Google Earth imagery with the SAR image deformation discontinuities, this study mapped and characterised more than 40 active faults in the three study areas, these include (1) the NE-SW trended sinistral strike-slip faults triggered by extension and (2) the NW-SE/W-E trended reverse faults triggered by maximum compression. The fault activity is characterised by subtle (minor) displacement rate value difference between the two sides of the fault plane. This research successfully improved the understanding of local structural geology and provided moderate guidance for the selection of HLW disposal sites in China. It was indicated that Xinchang has the highest tectonic stability, and this is then followed by Jiujing and Suanjingzi. This kind of displacement rate difference is possible due to the angle difference towards the Sanweishan Fault Zone. To trace and characterise the undiscovered active fault planes, the PSInSAR approach also benefits the prediction of earthquake by improving the positioning of the potential epicentres.Open Acces

    On the Exploitation of CubeSats for Highly Accurate and Robust Single-Pass SAR Interferometry

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    Highly accurate digital elevation models (DEMs) from spaceborne synthetic aperture radar (SAR) interferometry are often affected by phase unwrapping errors. These errors can be resolved by the use of additional interferograms with different baselines, but this requires additional satellites in a single-pass configuration, resulting in higher cost and system complexity, or additional passes of the satellites, which affects mission planning and makes the system less suitable for monitoring fast-changing phenomena. This work proposes augmenting a bistatic SAR interferometer with one or more receive-only CubeSats, whose images are used to form an additional interferogram with a small baseline, making the system robust to unwrapping errors. In spite of the lower quality of the CubeSat images due to their small antenna aperture, this additional information can be used to detect and resolve phase unwrapping errors in the DEM without impacting its resolution or accuracy. A processing scheme for the phase unwrapping correction is presented along with a theoretical model for its performance. Finally, a design example is presented and discussed along with a simulation based on TanDEM-X data. It is also shown that CubeSat add-ons allow further increasing the baseline and thus improving the accuracy of DEMs. This concept represents a cost-effective solution for the generation of highly accurate, robust DEMs and paves the way to distributed SAR interferometric concepts based on CubeSats

    Estimation of change in forest variables using synthetic aperture radar

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    Large scale mapping of changes in forest variables is needed for both environmental monitoring, planning of climate actions and sustainable forest management. Remote sensing can be used in conjunction with field data to produce wall-to-wall estimates that are practically impossible to produce using traditional field surveys. Synthetic aperture radar (SAR) can observe the forest independent of sunlight, clouds, snow, or rain, providing reliable high frequency coverage. Its wavelength determines the interaction with the forest, where longer wavelengths interact with larger structures of the trees, and shorter wavelengths interact mainly with the top part of the canopy, meaning that it can be chosen to fit specific applications. This thesis contains five studies conducted on the Remningstorp test site in southern Sweden. Studies I – III predicted above ground biomass (AGB) change using long wavelength polarimetric P- (in I) and L-band (in I – III) SAR data. The differences between the bands were small in terms of prediction quality, and the HV polarization, just as for AGB state prediction, was the polarization channel most correlated with AGB change. A moisture correction for L-band data was proposed and evaluated, and it was found that certain polarimetric measures were better for predicting AGB change than all of the polarization channels together. Study IV assessed the detectability of silvicultural treatments in short wavelength TanDEM-X interferometric phase heights. In line with earlier studies, only clear cuts were unambiguously distinguishable. Study V predicted site index and stand age by fitting height development curves to time series of TanDEM-X data. Site index and age were unbiasedly predicted for untreated plots, and the RMSE would likely decrease with longer time series. When stand age was known, SI was predicted with an RMSE comparable to that of the field based measurements. In conclusion, this thesis underscores SAR data's potential for generalizable methods for estimation of forest variable changes

    ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

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    Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

    Novel Approaches in Landslide Monitoring and Data Analysis

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    Significant progress has been made in the last few years that has expanded the knowledge of landslide processes. It is, therefore, necessary to summarize, share and disseminate the latest knowledge and expertise. This Special Issue brings together novel research focused on landslide monitoring, modelling and data analysis

    Remote sensing of sea ice properties and dynamics using SAR interferometry

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    Landfast ice is attached to the coastline and islands and stays immobile over most of the ice season. It is an important element of polar ecosystems and plays a vital role as a marine habitat and in life of local people and economy through offshore technology. Landfast ice is routinely used for on-ice traffic, tourism, and industry, and it protects coasts from storms in winter from erosion. However, landfast ice can break or experience deformation in order of centimeters to meters, which can be dangerous for the coastline and man-made structures, beacons, on-ice traffic, and represents a safety risk for working on the ice and local people. Therefore, landfast ice deformation and stability are important topics in coastal engineering and sea ice modeling. In the framework of this dissertation, InSAR (SAR Interferometry) technology has been applied for deriving landfast ice displacements (publication I), and mapping sea ice morphology, topography and its temporal change (publication III). Also, advantages of InSAR remote sensing in sea ice classification compared to backscatter intensity were demonstrated (publications II and IV). In publication I, for the first time, Sentinel-1 repeat-pass InSAR data acquired over the landfast ice areas were used to study the landfast ice displacements in the Gulf of Bothnia. An InSAR pair with a temporal baseline of 12 days acquired in February 2015 was used. In the study, the surface of landfast ice was stable enough to preserve coherence over the 12-day period, enabling analysis of the interferogram. The advantage of this long temporal baseline is in separating the landfast ice from drift ice and detecting long-term trends in deformation maps. The interferogram showed displacements of landfast ice on the order of 40 cm. The main factor seemed to be compression by drift ice, which was driven against the landfast ice boundary by strong winds from southwest. Landfast ice ridges can hinder ship navigation, but grounded ridges help to stabilize the ice cover. In publication III, ridge formation and displacements in the landfast ice near Utqiaġvik, Alaska were examined. The phase signatures of two single-pass bistatic X-band SAR (Synthetic Aperture Radar) image pairs acquired by TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) satellite on 13 and 24 January 2012 were analyzed. Altogether six cases were identified with ridge displacement in four and formation in two cases under onshore compression. The ridges moved approximately 0.6 and 3.7 km over the study area and ridge formation reached up to 1 meter in upward. The results well corresponded with the locations identified as convergence zones retrieved from the drift algorithm generated by a SAR-based sea ice-tracking algorithm, backscatter intensity images and coastal radar imagery. This method could potentially be used in future to evaluate sea ice stability and ridge formation. A bistatic InSAR pair acquired by the TanDEM-X mission in March 2012 over the Bothnian Bay was used in two further studies (publications II and IV). The potential of X-band InSAR imagery for automated sea ice classification was evaluated. The first results were presented in publication II and the data were further elaborated in publication IV. The backscatter intensity, coherence magnitude and InSAR-phase features, as well as their different combinations, were used as the informative features in classification experiments. In publication II, the purpose was to assess ice properties on the scale used in ice charting, with ice types based on ice concentration and sea ice morphology, while in publication IV, a detailed small-scale analysis was performed. In addition, the sampling design was different in these publications. In publication II, to achieve the best discrimination between open water and several sea-ice types, RF (Random Forests) and ML (Maximum likelihood) classifiers were employed. The best overall accuracies were achieved by combining backscatter intensity & InSAR-phase using RF approach and backscatter intensity & coherence-magnitude using ML approach. The results showed the advantage of adding InSAR features to backscatter intensity for sea ice classification. In the further study (publication IV), a set of state-of-the-art classification approaches including ML, RF and SVM (Support Vector Machine) classifiers were used to achieve the best discrimination between open water and several sea-ice types. Adding InSAR-phase and coherence magnitude to backscatter intensity improved the OA (Overall Accuracy) compared to using only backscatter intensity. The RF and SVM algorithms gave somewhat larger OA compared to ML at the expense of a somewhat longer processing time. Results of publications II and IV demonstrate InSAR features have potential to improve sea ice classification. InSAR could be used by operational ice services to improve mapping accuracy of automated sea ice charting with statistical and machine learning classification approaches.Viime vuosikymmeninä satelliittivälitteisestä SAR-tutkasta on tullut erittäin tärkeä työkalu merijään kaukokartoituksessa. Tämän tutka perustuu sähkömagneettisten aaltojen sirontaan kiinnostavasta kohteesta takaisin tutkaan, mitä seuraa signaalin voimakkuuden mittaaminen. SAR-tutkat käyttävät synteettistä antennia, joka perustuu satelliitin liikkeeseen, mikä mahdollistaa tarkkojen, korkean erotuskyvyn kuvien tuottamisen. SAR-anturit mittaavat myös signaalin vaihetta, jota käytetään interferometria tekniikassa pinnan topografian ja siirtymien laskemiseen eri sovelluksissa, kuten maan muodonmuutoksissa, tarkassa kartoituksessa, maanjäristyksen arvioinnissa ja tulivuorenpurkauksien tarkkailussa. Interferometri tekniikkaa käytettiin tässä opinnäytetyössä pienten jäänsiirtymien analysointiin kiintojäävyöhykkeellä, joka on kiinni rantaviivassa ja saarissa eikä juuri liiku tuulien tai virtausten mukana. Kiintojääalueilla on pohjaan tarttuneita jäävalleja, jotka edistävät kiintojääpeitteen vakautumista. Kiintojäällä on tärkeä rooli merellisenä elinympäristönä, maankäytön kysymyksissä sekä paikallisten ihmisten elämässä ja meritekniikassa. Kiintojää voi murtua liikahdella useita metrejä, mikä voi olla vaarallista rakenteille, majakoille ja jäällä liikkujille. Tässä väitöskirjassa Sentinel-1A ja TanDEM-X satelliitteja ja interferometri tekniikkaa on käytetty arktisilla alueilla ja Itämerellä mittaamaan kiintojään muodonmuutoksia ja siirtymiä sekä niihin liittyviä mekanismeja. Lisäksi on tutkittu automaattista merijääluokitusta interferometrian apuohjelmiston avulla, mikä laajentaa operatiivisten merijääpalvelujen tutkahavaintojen käyttöä. Sentinel-1A:n avulla voitiin tarkastella 12 päivän pituisia muutoksia Pohjanlahden kiintojäävyöhykkeellä, kun interferometria tekniikka mittasi voimakkaan tuulen aiheuttaman 40 cm:n siirtymiä. Pohjoisella jäämerellä voitiin tunnistaa jäävallien siirtymiä ja muodostumia. Vallit siirtyivät noin 0,6 ja 3,7 km matkoja ja muodostuessaan ne kasvoivat metrin korkeuteen. Interferometri tekniikan lisääminen tutkakuvauksen analyysiin osoitti potentiaalin parantaa automaattisen merijääkartoituksen kartoituksen tarkkuutta tilastollisilla ja koneoppimiseen perustuvan luokittelun menetelmillä. Tulevaisuuden työnä merijään luokituksessa ja vallitutkimuksissa olisi suositeltavaa käyttää erilaisia ja useampia tutkakuvauksen geometrioita sekä erilaisia jääolosuhteita eri sääolosuhteiden vallitessa
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