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  • 1.
    Alves, Dimas, I
    et al.
    Fed Univ Pampa UNIPAMPA, BRA.
    Muller, Cristian
    Fed Univ Pampa UNIPAMPA, BRA.
    Vu, Viet Thuy
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    de Jesus, Pablo Kunz
    Aeronaut Inst Technol ITA, BRA.
    Machado, Renato
    Aeronaut Inst Technol ITA, BRA.
    Uchoa-Filho, Bartolomeu E.
    Fed Univ Santa Catarina UFSC, BRA.
    Incoherent Change Detection Methods for Wavelength-Resolution SAR Image Stacks Based on Masking Techniques2020Ingår i: 2020 IEEE National Radar Conference - Proceedings, IEEE , 2020, artikel-id 9266431Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents two incoherent change detection methods for wavelength-resolution synthetic aperture radars (SAR) image stacks based on masking techniques. The first technique proposed is the Simple Masking Detection (SMD). This method uses the statistical behavior of pixels-sets in the image stack to create a binary mask, which is used to remove pixels that are not related to changes in a surveillance image from the same interest region. The second technique is the Multiple Concatenated Masking Detection (MCMD), which produces a more selective mask than the SMD by concatenating multiple masks from different image stacks. The MCMD can be used in specific applications where multiple stacks share common patterns of target deployments. Both proposed techniques were evaluated using 24 incoherent SAR images obtained by the CARABAS II system. The experimental results revealed that the proposed detection methods have better performance in terms of probability of detection and false alarm rate when compared with other change detection techniques, especially for high detection probabilities scenarios.

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  • 2.
    Alves, Dimas I
    et al.
    Fed Univ Pampa UNIPAMPA, BRA.
    Palm, Bruna
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Vu, Viet Thuy
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Machado, Renato
    Aeronaut Inst Technol ITA, BRA.
    Uchoa-Filho, Bartolomeu F.
    Fed Univ Santa Catarina UFSC, BRA.
    Dammert, Patrik
    Saab Elect Def Syst, SWE.
    Hellsten, Hans
    Saab Elect Def Syst, SWE.
    A Statistical Analysis for Wavelength-Resolution SAR Image Stacks2020Ingår i: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 17, nr 2, s. 227-231Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This letter presents a clutter statistical analysis for stacks of wavelength-resolution synthetic aperture radar (SAR) images. Each image stack consists of SAR images generated by the same sensor, using the same flight track illuminating the same scene but with a time separation between the illuminations. We test three candidate statistical distributions for time changes in the stack, namely, Rician, Rayleigh, and log-normal. The tests results reveal that the Rician distribution is a very good candidate for modeling stack of wavelength-resolution SAR images, where 98.59 & x0025; of the tested samples passed the Anderson-Darling (AD) goodness-of-fit test. Also, it is observed that the presence of changes in the ground scene is related to the tested samples that have failed in the AD test for the Rician distribution hypothesis.

  • 3.
    Atif, Yacine
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Ding, Jianguo
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Lindström, Birgitta
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Jeusfeld, Manfred
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Yuning, Jiang
    Högskolan i Skövde, Institutionen för informationsteknologi.
    Brax, Christoffer
    CombiTech AB, Skövde, Sweden.
    Gustavsson, Per M.
    CombiTech AB, Skövde, Sweden.
    Cyber-Threat Intelligence Architecture for Smart-Grid Critical Infrastructures Protection2017Konferensbidrag (Refereegranskat)
    Abstract [en]

    Critical infrastructures (CIs) are becoming increasingly sophisticated with embedded cyber-physical systems (CPSs) that provide managerial automation and autonomic controls. Yet these advances expose CI components to new cyber-threats, leading to a chain of dysfunctionalities with catastrophic socio-economical implications. We propose a comprehensive architectural model to support the development of incident management tools that provide situation-awareness and cyber-threats intelligence for CI protection, with a special focus on smart-grid CI. The goal is to unleash forensic data from CPS-based CIs to perform some predictive analytics. In doing so, we use some AI (Artificial Intelligence) paradigms for both data collection, threat detection, and cascade-effects prediction. 

  • 4.
    da Silva, Fabiano G.
    et al.
    Aeronautics Institute of Technology, BRA.
    Ramos, Lucas P.
    Aeronautics Institute of Technology, BRA.
    Palm, Bruna
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Alves, Dimas I.
    Aeronautics Institute of Technology, BRA.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Machado, Renato
    Aeronautics Institute of Technology, BRA.
    Hybrid Feature Extraction Based on PCA and CNN for Oil Rig Classification in C-Band SAR Imagery2022Ingår i: Proceedings of SPIE - The International Society for Optical Engineering / [ed] Dijk J., SPIE - International Society for Optical Engineering, 2022, artikel-id 122760GKonferensbidrag (Refereegranskat)
    Abstract [en]

    Feature extraction techniques play an essential role in classifying and recognizing targets in synthetic aperture radar (SAR) images. This article proposes a hybrid feature extraction technique based on convolutional neural networks and principal component analysis. The proposed method is used to extract features of oil rigs and ships in C-band synthetic aperture radar polarimetric images obtained with the Sentinel-1 satellite system. The extracted features are used as input in the logistic regression (LR), support vector machine (SVM), random forest (RF), naive Bayes (NB), decision tree (DT), and k-nearest-neighbors (kNN) classification algorithms. Furthermore, the statistical tests of Kruskal-Wallis and Dunn were considered to show that the proposed extraction algorithm has a significant impact on the performance of the classifiers. © 2022 SPIE.

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  • 5.
    da Silva, Fabiano G.
    et al.
    Aeronautics Institute of Technology (ITA), BRA.
    Ramos, Lucas P.
    Aeronautics Institute of Technology (ITA), BRA.
    Palm, Bruna
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Machado, Renato
    Aeronautics Institute of Technology (ITA), BRA.
    Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images2022Ingår i: Remote Sensing, E-ISSN 2072-4292, Vol. 14, nr 13, artikel-id 2966Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This article aims at performing maritime target classification in SAR images using machine learning (ML) and deep learning (DL) techniques. In particular, the targets of interest are oil platforms and ships located in the Campos Basin, Brazil. Two convolutional neural networks (CNNs), VGG-16 and VGG-19, were used for attribute extraction. The logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), naive Bayes (NB), neural networks (NET), and AdaBoost (ADBST) schemes were considered for classification. The target classification methods were evaluated using polarimetric images obtained from the C-band synthetic aperture radar (SAR) system Sentinel-1. Classifiers are assessed by the accuracy indicator. The LR, SVM, NET, and stacking results indicate better performance, with accuracy ranging from 84.1% to 85.5%. The Kruskal–Wallis test shows a significant difference with the tested classifier, indicating that some classifiers present different accuracy results. The optimizations provide results with more significant accuracy gains, making them competitive with those shown in the literature. There is no exact combination of methods for SAR image classification that will always guarantee the best accuracy. The optimizations performed in this article were for the specific data set of the Campos Basin, and results may change depending on the data set format and the number of images. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

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  • 6.
    Flyckt, Jonatan
    et al.
    Jönköping University, SWE.
    Andersson, Filip
    Jönköping University, SWE.
    Lavesson, Niklas
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för programvaruteknik.
    Nilsson, Liselott
    Swedish Forest Agency, SWE.
    Ågren, Anneli M.
    Swedish University of Agricultural Sciences, SLU, SWE.
    Detecting ditches using supervised learning on high-resolution digital elevation models2022Ingår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 201, artikel-id 116961Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Drained wetlands can constitute a large source of greenhouse gas emissions, but the drainage networks in these wetlands are largely unmapped, and better maps are needed to aid in forest production and to better understand the climate consequences. We develop a method for detecting ditches in high resolution digital elevation models derived from LiDAR scans. Thresholding methods using digital terrain indices can be used to detect ditches. However, a single threshold generally does not capture the variability in the landscape, and generates many false positives and negatives. We hypothesise that, by combining the digital terrain indices using supervised learning, we can improve ditch detection at a landscape-scale. In addition to digital terrain indices, additional features are generated by transforming the data to include neighbouring cells for better ditch predictions. A Random Forests classifier is used to locate the ditches, and its probability output is processed to remove noise, and binarised to produce the final ditch prediction. The confidence interval for the Cohen's Kappa index ranges [0.655, 0.781] between the evaluation plots with a confidence level of 95%. The study demonstrates that combining information from a suite of digital terrain indices using machine learning provides an effective technique for automatic ditch detection at a landscape-scale, aiding in both practical forest management and in combatting climate change. © 2022 The Authors

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  • 7.
    Javadi, Saleh
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Palm, Bruna
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Vu, Viet Thuy
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Sjögren, Thomas
    Swedish Defense Research Agency (FOI).
    Harbour Area Change Detection and Analysis Using SAR Images from a Recent Measurement Campaign2023Ingår i: Proceedings of the IEEE Radar Conference 2023, Institute of Electrical and Electronics Engineers (IEEE), 2023Konferensbidrag (Refereegranskat)
    Abstract [en]

    Synthetic aperture radar (SAR) data are widely used for remote sensing applications, such as change detection and environmental monitoring. This paper presents a recent measurement campaign for SAR images using the LORA system and investigates the applicability of the collected data for change detection. The region of interest in this study is a busy commercial harbour area in the south of Sweden. During the measurements, there were significant changes on the ground in the parking lot as trucks were disembarking a ship. The obtained SAR images were first filtered to have similar regions of interest in the Fourier domain to increase the coherence magnitude. Then, a constant false alarm rate (CFAR) algorithm was employed to detect changes with respect to trucks. In addition, optical aerial images were collected during this measurement campaign and were utilized to adjust the CFAR detection threshold. As a result, all the changed and unchanged regions corresponding to the selected targets were detected successfully. Moreover, a pattern of trucks’ utilization of the harbour’s parking lot during this peak time was obtained. The results demonstrate the applicability of the data from the ongoing measurement campaign and the possibility of further algorithm development for target detection and classification.

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  • 8.
    Lidberg, William
    et al.
    Swedish University of Agricultural Sciences.
    Paul, Siddhartho Shekhar
    Swedish University of Agricultural Sciences.
    Westphal, Florian
    Jonkoping University.
    Richter, Kai Florian
    Umea University.
    Lavesson, Niklas
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för programvaruteknik.
    Melniks, Raitis
    Latvian State Forest Research Institute Silava, Latvia.
    Ivanovs, Janis
    Latvian State Forest Research Institute Silava, Latvia.
    Ciesielski, Mariusz
    Forest Research Institute, Poland.
    Leinonen, Antti
    Finnish Forest Centre, Finland.
    Agren, Anneli M.
    Swedish University of Agricultural Sciences.
    Mapping Drainage Ditches in Forested Landscapes Using Deep Learning and Aerial Laser Scanning2023Ingår i: Journal of irrigation and drainage engineering, ISSN 0733-9437, E-ISSN 1943-4774, Vol. 149, nr 3, artikel-id 04022051Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Extensive use of drainage ditches in European boreal forests and in some parts of North America has resulted in a major change in wetland and soil hydrology and impacted the overall ecosystem functions of these regions. An increasing understanding of the environmental risks associated with forest ditches makes mapping these ditches a priority for sustainable forest and land use management. Here, we present the first rigorous deep learning-based methodology to map forest ditches at regional scale. A deep neural network was trained on airborne laser scanning data (ALS) and 1,607 km of manually digitized ditch channels from 10 regions spread across Sweden. The model correctly mapped 86% of all ditch channels in the test data, with a Matthews correlation coefficient of 0.78. Further, the model proved to be accurate when evaluated on ALS data from other heavily ditched countries in the Baltic Sea Region. This study leads the way in using deep learning and airborne laser scanning for mapping fine-resolution drainage ditches over large areas. This technique requires only one topographical index, which makes it possible to implement on national scales with limited computational resources. It thus provides a significant contribution to the assessment of regional hydrology and ecosystem dynamics in forested landscapes.

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  • 9.
    Ludwig Barbosa, Vinícius
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Effects of Small-Scale Ionospheric Irregularities on GNSS Radio Occultation Signals: Evaluations Using Multiple Phase Screen Simulator2019Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Radio Occultation (RO) is a remote sensing technique which uses Global Navigation Satellite System (GNSS) signals tracked by a Low-Earth Orbit (LEO) satellite to sound the earth's atmosphere both in low (troposphere, stratosphere) and high (ionosphere) altitudes. GNSS-RO provides global coverage and SI traceable measurements of atmospheric data with high-vertical resolution. Refractivity, dry temperature, pressure and water vapour profiles retrieved from RO measurements have a relevant contribution in Numerical Weather Prediction (NWP) systems and in climate-monitoring.

    Due to the partial propagation through the ionosphere, a systematic bias is added to the lower atmospheric data product. Most of this contribution is removed by a linear combination of data for two frequencies. In climatology studies, one can apply a second-order correction - so called κ-correction - which relies on a priori information on the conditions in the ionosphere. However, both approaches do not remove high-order terms in the error due to horizontal gradient and earth's geomagnetic fields. The remaining residual ionospheric error (RIE) and its systematic bias in RO atmospheric data is a well-known issue and its mitigation is an open research topic.

    In this licentiate dissertation, the residual ionospheric error after the standard correction is evaluated with computational simulations using a wave optics propagator (WOP). Multiple Phase Screen (MPS) method is used to simulate occultation events in different ionospheric scenarios, e.g. quiet and disturbed conditions. Electron density profiles (EDP) assumed in simulations are either defined by analytical equations or measurements. The disturbed cases are modelled as small-scale irregularities within F-region in two different ways: as sinusoidal fluctuations; and by using a more complex approach, where the irregularities follow a single-slope power-law that yields moderate to strong scintillation in the signal amplitude. Possible errors in MPS simulations assuming long segment of orbit and ionosphere are also evaluated.

    The results obtained with the sinusoidal disturbances show minor influence in the RIE after the standard correction, with the major part of the error due to the F-region peak. The implementation of the single-slope power-law is validated and the fluctuations obtained in simulation show good agreement to the ones observed in RO measurements. Finally, an alternative to overcome limitations in MPS simulations considering occultations with long segment of orbit and ionosphere is introduced and validated.

    The small-scale irregularities modelled in F-region with the power-law can be added in simulations of a large dataset subjected to κ-correction, in order to evaluate the RIE bending angle and the consequences in atmospheric parameters, e.g. temperature.

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  • 10.
    Ludwig Barbosa, Vinícius
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    On the Ionospheric Influence on GNSS Radio Occultation Signals: Modelling and Assessment2022Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Radio Occultation (RO) is a well-established remote sensing technique that uses Global Navigation Satellite System (GNSS) signals to sound the Earth’s atmosphere. GNSS-RO measurements provide high-resolution, vertical profiles of physical parameters from the lower atmosphere (troposphere and stratosphere), e.g., refractivity, dry temperature, pressure, and water vapour, with primary application in weather forecasting and climatology models. The upper atmosphere (ionosphere) is also sounded during measurements, in which information about total electron content, electron density profiles, and scintillation indices compose the RO ionospheric data product.

    The ionosphere is a dispersive medium composed of ionized particles. It is extensively conditioned by Solar activity and shows seasonal, geographical, and day- and night-time variation. Despite the benefit of the upper atmospheric data, the ionosphere influences the retrievals in the lower atmosphere by (i) adding an inherent systematic bias in bending angles, i.e., residual ionospheric error (RIE), and (ii) disturbing the signal amplitude and phase, i.e., scintillation, in the presence of irregularities regions on the electron density along the ray path, e.g., equatorial plasma bubbles. In this dissertation, both aspects are investigated by modelling the equatorial ionosphere, and its small-scale irregularities in simulations of occultation events to (i) reproduce the effects observed in measurements and (ii) assess methods that can extract information about the ionosphere and support its monitoring and modelling.

    The multiple phase screen method was applied to model the GNSS signal propagation through quiet and disturbed ionospheric conditions. The small-scale irregularities in the F-region were modelled by a single slope power law to yield moderate to strong scintillation in the signals. Results were assessed according to the amplitude and phase scintillation indices, RIE, the standard deviation of the retrieved bending angles, and power spectral density (PSD). A subset of these parameters was taken as features to train a classifier based on the support vector machine algorithm. The purpose of this model was to detect RO measurements affected by ionospheric scintillation. Specifically, those in which PSD could provide further information about the irregularities according to the scintillation theory. Additionally, the back propagation (BP) method and its capability to estimate the mean distance between the receiver and irregularities were evaluated.

    Applying spectral analysis techniques to RO measurements may contribute to the characterization of small-scale irregularities in equatorial plasma bubbles. The results from simulations applying the single-slope power law to model the irregularities showed a good agreement with the selected cases. The automatic detection of occultations affected by ionospheric irregularities has achieved similar performance to models trained with ground-based measurements. Furthermore, the BP method can add the estimation of the mean location to the spectral analysis information. Such tools can enlarge the amount of ionospheric data retrieved -- especially for occultations with extended vertical range and when combined with other sounding techniques.

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  • 11.
    Ludwig Barbosa, Vinícius
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Rasch, Joel
    Beyond Gravity Ab, Sweden.
    Carlstrom, Anders
    Beyond Gravity Ab, Sweden.
    Christensen, Jacob
    Beyond Gravity Ab, Sweden.
    Vu, Viet Thuy
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Location of Ionospheric Irregularities in Extended GNSS-RO Measurements Using Back Propagation Method2023Ingår i: 2023 35th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2023, Institute of Electrical and Electronics Engineers (IEEE), 2023Konferensbidrag (Refereegranskat)
    Abstract [en]

    Besides providing electron density profiles (EDP), GNSS Radio Occultation (GNSS-RO) measurements allow monitoring the frequency and the areas where ionospheric scintillations occur. In this work, RO measurements composing an experimental data set are processed with the back propagation (BP) method to estimate the location of sporadic E-clouds and equatorial plasma bubbles (EPB). The data set includes non-conventional measurements tracked up to 600 km (generally around 80 km), covering F-region heights, shortly before MetOp-A was decommissioned. Results indicate the combination of extended occultations and the BP method is promising for monitoring the occurrence and characterizing ionospheric irregularities in the F-region and the E-region. © 2023 International Union of Radio Science.

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  • 12.
    Ludwig Barbosa, Vinícius
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Rasch, Joel
    Molflow, SWE.
    Carlström, Anders
    RUAG Space AB, SWE.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Vu, Viet Thuy
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    GNSS Radio Occultation Simulation Using Multiple Phase Screen Orbit Sampling2020Ingår i: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 17, nr 8, s. 1323-1327, artikel-id 8869926Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Wave optics propagators (WOPs) are commonlyused to describe the propagation of radio signals through earth’satmosphere. In radio occultation (RO) context, multiple phasescreen (MPS) method has been used to model the effects of theatmosphere in Global Navigation Satellite System (GNSS) signalsduring an occultation event. WOP implementation includes,in addition to MPS, a diffraction integral as the final step tocalculate the radio signal measured in the low-earth orbit (LEO)satellite. This approach considers vacuum as the propagationmedium at high altitudes, which is not always the case when theionosphere is taken into account in simulations. An alternativeapproach is using MPS all the way to LEO in order to samplethe GNSS signal in orbit. This approach, named MPS orbitsampling (MPS-OS), is evaluated in this letter. Different scenariosof setting occultation assuming a short segment of the LEO orbithave been simulated using MPS and MPS-OS. Results have beencompared to Abel transform references. Furthermore, a longsegment scenario has been evaluated as well. A comparison ofbending angle (BA) and residual ionospheric error (RIE) showsthe equivalence between MPS and MPS-OS results. The mainapplication of MPS-OS should be in occultation events with longsegments of orbit and including ionosphere, in which a standardWOP may not be appropriate.

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    GNSS Radio Occultation Simulation Using Multiple Phase Screen Orbit Sampling
  • 13.
    Ludwig Barbosa, Vinícius
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Sievert, Thomas
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Rasch, Joel
    Molflow, SWE.
    Carlström, Anders
    RUAG Space AB, SWE.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Vu, Viet Thuy
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Evaluation of Ionospheric Scintillation in GNSS Radio Occultation Measurements and Simulations2020Ingår i: Radio Science, ISSN 0048-6604, E-ISSN 1944-799X, Vol. 55, nr 8, artikel-id e2019RS006996Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Like any other system relying on trans-ionospheric propagation, GNSS Radio Occultation (GNSS-RO) is affected by ionospheric conditions during measurements. Regions of plasma irregularities in F-region create abrupt gradients in the distribution of ionized particles. Radio signals propagated through such regions suffer from constructive and destructive contributions in phase and amplitude, known as scintillations. Different approaches have been proposed in order to model and reproduce the wave propagation through ionospheric irregularities. We present simulations considering an one-component inverse power-law model of irregularities integrated with Multiple Phase Screen (MPS) propagation. In this work, the capability of the scintillation model to reproduce features in the signal amplitude of low latitude MetOp measurements in the early hours of DOY 76, 2015 (St. Patrick’s Day geomagnetic storm) is evaluated. Power spectral density (PSD) analysis, scintillation index, decorrelation time and standard deviation of neutral bending angle are considered in the comparison between the simulations and RO measurements. The results validate the capability of the simulator to replicate an equivalent total integrated phase variance in cases of moderate to strong scintillation.

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  • 14.
    Moghimi, Armin
    et al.
    K. N. Toosi University of Technology, IRN.
    Sarmadian, Amin
    K. N. Toosi University of Technology, IRN.
    Mohammadzadeh, Ali
    K. N. Toosi University of Technology, IRN.
    Celik, Turgay
    University of the Witwatersrand, ZAF.
    Amani, Meisam
    Wood Environment and Infrastructure Solutions, CAN.
    Kusetogullari, Hüseyin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Distortion Robust Relative Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Using Image Features2022Ingår i: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 60, artikel-id 5400820Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this article, we propose a novel framework to radiometrically correct unregistered multisensor image pairs based on the extracted feature points with the KAZE detector and the conditional probability (CP) process in the linear model fitting. In this method, the scale, rotation, and illumination invariant radiometric control set samples (SRII-RCSS) are first extracted by the blockwise KAZE strategy. They are then distributed uniformly over both textured and texture-less land use/land cover (LULC) using grid interpolation and a set of nearest-neighbors. Subsequently, SRII-RCSS are scored by a similarity measure, and the histogram of the scores is then used to refine SRII-RCSS. The normalized subject image is produced by adjusting the subject image to the reference image using the CP-based linear regression (CPLR) based on the optimal SRII-RCSS. The registered normalized image is finally generated by registration of the normalized subject image to the reference image through a two-pass registration method, namely affine-B-spline and, then, it is enhanced by updating the normalization coefficient of CPLR based on the SRII-RCSS. In this study, eight multitemporal data sets acquired by inter/intra satellite sensors were used in tests to comprehensively assess the efficiency of the proposed method. Experimental results show that the proposed method outperforms the existing state-of-the-art relative radiometric normalization (RRN) methods both qualitatively and quantitatively, indicating its capability for RRN of unregistered multisensor image pairs. IEEE

  • 15.
    Molin, R.D.
    et al.
    Federal University of Santa Maria, Brazil.
    Fabrin, A.C.F.
    Federal University of Santa Maria, Brazil.
    Sperotto, P.
    Federal University of Santa Maria, Brazil.
    Alves, D.I.
    Federal University of Santa Maria, Brazil.
    Bayer, F.M.
    Federal University of Santa Maria, Brazil.
    Machado, Renato
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Dammert, P.
    Saab AB, SWE.
    Hellsten, H.
    Saab AB, SWE.
    Ulander, L
    Totalförsvarets Forskningsinstitut, SWE.
    Iterative Change Detection Algorithm for Low-Frequency UWB SAR2016Konferensbidrag (Refereegranskat)
  • 16.
    Sievert, Thomas
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    GNSS Radio Occultation Inversion Methods and Reflection Observations in the Lower Troposphere2019Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    GNSS Radio Occultation (GNSS-RO) is an opportunistic Earth sensing technique where GNSS signals passing through the atmosphere are received in low Earth orbit and processed to extract meteorological parameters. As signals are received along an orbit, the measured Doppler shift is transformed to a bending angle profile (commonly referred to as bending angle retrieval), which, in turn, is inverted to a refractivity profile. Thanks to its high vertical resolution and SI traceability, GNSS-RO is an important complement to other Earth sensing endeavors. In the lower troposphere, GNSS-RO measurements often get degraded and biased due to sharp refractive gradients and other complex structures. The main objective of this thesis is to explore contemporary retrieval methods such as phase matching and full spectrum inversion to improve their performance in these conditions. To avoid the bias caused by the standard inversion, we attempt to derive additional information from the amplitude output of the examined retrieval operators. While simulations indicate that such information could be found, it is not immediately straightforward how to achieve this with real measurements. The approach chosen is to examine reflected signal components and their effect on the amplitude output.

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  • 17.
    Sievert, Thomas
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Rasch, Joel
    Molflow, SWE.
    Carlström, Anders
    RUAG Space AB, SWE.
    Ludwig Barbosa, Vinícius
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Vu, Viet Thuy
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Using A Sliding Window Phase Matching Method for Imaging of GNSS Radio Occultation Signals2021Ingår i: Remote Sensing, E-ISSN 2072-4292, Vol. 13, nr 5, artikel-id 970Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Global Navigation Satellite System Radio Occultation (GNSS-RO) is a technique used to sound the atmosphere and derive vertical profiles of refractivity. Signals from GNSS satellites are received in a low-Earth orbit, and they are then processed to produce bending angle profiles, from which meteorological parameters can be retrieved. Generating two-dimensional images in the form of spectrograms from GNSS-RO signals is commonly done to, for instance, investigate reflections or estimate signal quality in the lower troposphere. This is typically implemented using, e.g., the Short-Time Fourier Transform (STFT) to produce a time-frequency representation that is subsequently transformed to bending angle (BA) and impact height (IH) coordinates by non-linear mapping. In this paper, we propose an alternative method based on a straightforward extension of the Phase Matching (PM) operator to produce two-dimensional spectral images in the BA-IH domain by applying a sliding window. This Sliding Window Phase Matching (SWPM) method generates the spectral amplitude on an arbitrary grid in BA and IH, e.g., along the coordinate axes. To illustrate, we show both SWPM and STFT methods applied to operational MetOp-A data. For SWPM we use a constant window in the BA-dimension, whereas for STFT we use a conventional constant time window. We show that the SWPM method produces the same result as STFT when the same window length is used for both methods. The sample points in impact parameter and bending angle are those generated by and the main advantage is that SWPM offers the user a convenient way to freely sample the BA-IH space. The cost for this is processing time that is somewhat longer than implementations based on the Fast Fourier Transform, such as the STFT method.

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  • 18.
    Sievert, Thomas
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Rasch, Joel
    Molflow, Gothenburg, SWE.
    Carlström, Anders
    RUAG Space AB, SWE.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Analysis of reflections in GNSS radio occultation measurements using the phase matching amplitude2018Ingår i: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 11, nr 1, s. 569-580Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    It is well-known that in the presence of super-refractive layers in the lower-tropospheric inversion of GNSSradio occultation (RO) measurements using the Abel trans-form yields biased refractivity profiles. As such it is problem-atic to reconstruct the true refractivity from the RO signal.Additional information about this lower region of the atmo-sphere might be embedded in reflected parts of the signal. Toretrieve the bending angle, the phase matching operator canbe used. This operator produces a complex function of theimpact parameter, and from its phase we can calculate thebending angle. Instead of looking at the phase, in this paperwe focus on the function’s amplitude. The results in this pa-per show that the signatures of surface reflections in GNSSRO measurements can be significantly enhanced when usingthe phase matching method by processing only an appropri-ately selected segment of the received signal. This signatureenhancement is demonstrated by simulations and confirmedwith 10 hand-picked MetOp-A occultations with reflectedcomponents. To validate that these events show signs of re-flections, radio holographic images are generated. Our resultssuggest that the phase matching amplitude carries informa-tion that can improve the interpretation of radio occultationmeasurements in the lower troposphere.

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  • 19.
    Sjogren, Thomas K.
    et al.
    Swedish Defence Research Agency, SWE.
    Vu, Viet Thuy
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Gustavsson, Anders
    Swedish Defence Research Agency, SWE.
    Change Detection for Monostatic Pursuit SAR GMTI-Theories and Experimental Results2022Ingår i: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 60, artikel-id 5235514Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Monostatic pursuit refers to the operating mode formed by two monostatic synthetic aperture radar (SAR) systems that follow an identical orbit with a separation in a time of several seconds. The detected changes between SAR scenes with several seconds of time difference are most likely the changes caused by ground moving targets. Hence, this operating mode opens an opportunity to detect ground moving targets by SAR change detection methods. This article investigates this possibility to detect ground moving targets using change detection and to combine change detection and ground moving target indication (GMTI) for GMTI. In this combination, a GMTI method will help to classify the detected changes obtained with a change detection method. Some GMTI results are provided in the article based on the measurements in the monostatic pursuit mode with deployed targets, conducted by TerraSAR-X and TanDEM-X in Sweden in early 2015.

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  • 20.
    Vu, Viet Thuy
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Pernstal, Thomas
    SafeRadar, Mölndal.
    Sjogren, Thomas K.
    Swedish Defense Research Agency.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Image extra-coregistration based on GIP for radar applications2022Ingår i: Proceedings - 2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022 / [ed] Bao V.N.Q., Ha T.M., Institute of Electrical and Electronics Engineers (IEEE), 2022, s. 274-278Konferensbidrag (Refereegranskat)
    Abstract [en]

    Image coregistration is a mandatory procedure for radar applications such as synthetic aperture radar change detection. The spatial coregistration can be easily handled with geo-reference, whereas radiometric coregistration requires much more effort due to the randomness of noise and clutter. The performance of a coregistration method is usually measured by the correlation coefficient that is desire to be close to unity. However, the currently used methods cannot provide coregistration with such correlation coefficient value. The paper introduces an image extra-coregistration method that helps to increase the correlation coefficient between the radar images close to unity. This strongly supports radar applications such as change detection. © 2022 IEEE.

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  • 21.
    Vu, Viet Thuy
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Pettersson, Mats
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Dahl, Mattias
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för matematik och naturvetenskap.
    Sjögren, Thomas K.
    Swedish Def Res Agcy, SWE.
    A MEASUREMENT CAMPAIGN IN HARBOR TO DETECT CHANGES OF ACTIVITIES2019Ingår i: 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), IEEE , 2019, s. 1494-1497Konferensbidrag (Refereegranskat)
    Abstract [en]

    The typical activities in a harbor mainly concern logistics, traffic and transportation that need to be monitored or optimized. This paper reports a measurement campaign whose goal is to evaluate the performance of activity surveillance in a harbor using satellite SAR systems. The measurements were conducted in the southern Baltic sea, around the Verko harbor in Karlskrona, Sweden in the middle of 2018. The SAR data acquisition was with TerraSAR-X/TanDEM-X operating in staring spotlight mode as high resolution was desired. For an accurate evaluation, all activities in the Verko harbor were video recorded by a drone during the satellite measurement time. Some radar corner reflectors were also deployed on the ground that are available for co-registration. The data associated with two measurements in the end of August and in the beginning of September is presented in this paper. The SAR change detection processing and the initial analysis show the possibility to monitor activities concerning logistics, traffic and transportation using satellite SAR.

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