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  • 1.
    Alves, Dimas I
    et al.
    Fed Univ Pampa UNIPAMPA, Brasil.
    Muller, Christian
    Fed Univ Pampa UNIPAMPA, Brasil.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Vu, Viet Thuy
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Machado, Renato
    Aeronaut Inst Technol ITA, Brasil.
    Bartolomeu F, Uchôa-Filho
    Fed Univ Santa Catarina UFSC, Brasil.
    Statistical Analysis for Wavelength-Resolution SARImage Stacks: New Case Studies2020In: XXXVIII SIMPÓSIO BRASILEIRO DE TELECOMUNICAÇÕES E PROCESSAMENTO DE SINAIS, 2020Conference paper (Refereed)
    Abstract [en]

    This paper presents new case studies for thestatistical analysis for wavelength resolution SAR image stacks.The statistical analysis considers the Anderson-Darling goodnessof-fit test in a set of pixel samples from the same position obtainedfrom a SAR image stack. The test is applied in wavelengthresolution SAR image stacks. The present work consists of twocase studies based on the use of multiple-pass stacks and TypeI error using the False Discovery Rate controlling procedures.In addition, an application scenario is presented for the studiedscenarios.

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  • 2.
    Alves, Dimas I
    et al.
    Fed Univ Pampa UNIPAMPA, BRA.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Vu, Viet Thuy
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    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 Stacks2020In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 17, no 2, p. 227-231Article in journal (Refereed)
    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.
    Alves, Dimas irion
    et al.
    Aeronautics Institute of Technology (ITA), Brazil.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Hellsten, Hans
    Halmstad University.
    Machado, Renato
    Aeronautics Institute of Technology (ITA), Brazil.
    Vu, Viet Thuy
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Dammert, Patrik
    Saab Surveillance, Saab AB, Gothenburg.
    Change Detection Method for Wavelength-Resolution SAR Images Based on Bayes’ Theorem: An Iterative Approach2023In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 84734-84743Article in journal (Refereed)
    Abstract [en]

    This paper presents an iterative change detection (CD) method based on Bayes’ theorem for very high-frequency (VHF) ultra-wideband (UWB) SAR images considering commonly used clutter-plus-noise statistical models. The proposed detection technique uses the information of the detected changes to iteratively update the data and distribution information, obtaining more accurate clutter-plus-noise statistics resulting in false alarm reduction. The Bivariate Rayleigh and Bivariate Gaussian distributions are investigated as candidates to model the clutter-plus-noise, and the Anderson-Darling goodness-of-fit test is used to investigate three scenarios of interest. Different aspects related to the distributions are discussed, the observed mismatches are analyzed, and the impact of the distribution chosen for the proposed iterative change detection method is analyzed. Finally, the proposed iterative method performance is assessed in terms of the probability of detection and false alarm rate and compared with other competitive solutions. The experimental evaluation uses data from real measurements obtained using the CARABAS II SAR system. Results show that the proposed iterative CD algorithm performs better than the other methods. Author

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  • 4.
    Berner, Jessica
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Five-factor model, technology enthusiasm and technology anxiety2023In: Digital Health, E-ISSN 2055-2076, Vol. 9Article in journal (Refereed)
    Abstract [en]

    Older adults need to participate in the digital society, as societal and personal changes and what they do with the remaining time that they have in their older years has an undeniable effect on motivation, cognition and emotion. Changes in personality traits were investigated in older adults over the period 2019–2021. Technology enthusiasm and technology anxiety are attitudes that affect the relationship to the technology used. The changes in the score of technology enthusiasm and technology anxiety were the dependent variables. They were investigated with personality traits, age, gender, education, whether someone lives alone, cognitive function, digital social participation (DSP) and health literacy as predictors of the outcome. The Edwards-Nunnally index and logistic regression were used. The results indicated that DSP, lower age, lower neuroticism and higher education were indicative of less technology anxiety. High DSP and high extraversion are indicative of technology enthusiasm. DSP and attitude towards technology seem to be key in getting older adults to stay active online. © The Author(s) 2023.

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  • 5.
    da Silva, Fabiano G.
    et al.
    Aeronautics Institute of Technology, BRA.
    Ramos, Lucas P.
    Aeronautics Institute of Technology, BRA.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Alves, Dimas I.
    Aeronautics Institute of Technology, BRA.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Machado, Renato
    Aeronautics Institute of Technology, BRA.
    Hybrid Feature Extraction Based on PCA and CNN for Oil Rig Classification in C-Band SAR Imagery2022In: Proceedings of SPIE - The International Society for Optical Engineering / [ed] Dijk J., SPIE - International Society for Optical Engineering, 2022, article id 122760GConference paper (Refereed)
    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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  • 6.
    da Silva, Fabiano G.
    et al.
    Aeronautics Institute of Technology (ITA), BRA.
    Ramos, Lucas P.
    Aeronautics Institute of Technology (ITA), BRA.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Machado, Renato
    Aeronautics Institute of Technology (ITA), BRA.
    Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images2022In: Remote Sensing, E-ISSN 2072-4292, Vol. 14, no 13, article id 2966Article in journal (Refereed)
    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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  • 7.
    da Silva, Fabiano Gabriel
    et al.
    Centro de Guerra Acústica e Eletrônica da Marinha (CGAEM), Brazil.
    Ramos, Lucas P.
    Instituto Tecnológico de Aeronáutica (ITA), Brazil.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Machado, Renato
    Instituto Tecnológico de Aeronáutica (ITA), Brazil.
    Hyperparameters Analysis of Machine Learning Techniques for Classification of Marine Targets in SAR Images2023In: Proceedings of the XX Brazilian Symposium on Remote Sensing: Anais do XX Simpósio Brasileiro de Sensoriamento Remoto, 2023, Vol. 20, p. 1095-1098, article id 155793Conference paper (Refereed)
    Abstract [en]

    Due to the extensive coastal area of Brazil, pattern recognition techniques based on artificial intelligence can search for targets at sea faster for surveillance, rescue, or illicit combat activities. This article presents a hyperparameter analysis of machine learning techniques to classify targets in SAR images. We considered a data set with vertical horizontal polarization SAR images from Campos Basin, Rio de Janeiro, to classify oil platforms and ships. The classification attributes are extracted through a convolutional neural network VGG-16 pre-trained with the ImageNet data set. Then, four machine learning techniques are evaluated, random forest, decision tree, k-nearest-neighbors, and logistic regression. As a metric for assessing the classifiers, accuracy (Acc) and area under the curve (AUC) are used. The grid search technique is used to identify the best combination of parameters of the classifiers with the highest Acc and AUC. Finally, the best result is the logistic regression classifier.

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  • 8.
    Javadi, Saleh
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Vu, Viet Thuy
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Performance Evaluation of Unsupervised Coregistration Algorithms for Multitemporal SAR Images2022In: International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 64-67Conference paper (Refereed)
    Abstract [en]

    In this paper, we present three algorithms for the multitemporal synthetic aperture radar (SAR) images coregistration. The proposed algorithms are a 2-D cross correlation, a 1-D parabolic based, and a 2-D projective transformation. The 2-D cross correlation algorithm is used to obtain coarse estimation of the displacement for coregistration. In the second method, two independent 1-D parabolic interpolations are calculated to refine the estimation of the peak location of the cross correlation matrix with subpixel accuracy. Finally, in the third method, a 2-D projective transformation is employed to align the SAR images using point correspondences and the cubic interpolation. The performance evaluation of these algorithms are provided based on the coherence magnitude and the absolute displacement error for a point target using a corner reflector in the scene. The experimental results obtained on real recorded multitemporal satellite SAR data demonstrate the effectiveness and the computational complexity of these algorithms. © 2022 IEEE.

  • 9.
    Javadi, Saleh
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Vu, Viet Thuy
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Sjögren, Thomas
    Swedish Defense Research Agency (FOI).
    Harbour Area Change Detection and Analysis Using SAR Images from a Recent Measurement Campaign2023In: Proceedings of the IEEE Radar Conference 2023, Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper (Refereed)
    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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  • 10.
    Palm, Bruna
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Bayer, Fabio
    Universidade Federal de Santa Maria, BRA.
    Cintra, Renato
    Universidade Federal de Per nambuco, BRA.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Machado, Renato
    Aeronautics Institute of Technology (ITA), BRA.
    Rayleigh Regression Model for Ground Type Detection in SAR Imagery2019In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 16, no 10, p. 1660-1664, article id 8681168Article in journal (Refereed)
    Abstract [en]

    This letter proposes a regression model for nonnegative signals. The proposed regression estimates the mean of Rayleigh distributed signals by a structure which includes a set of regressors and a link function. For the proposed model, we present: 1) parameter estimation; 2) large data record results; and 3) a detection technique. In this letter, we present closed-form expressions for the score vector and Fisher information matrix. The proposed model is submitted to extensive Monte Carlo simulations and to the measured data. The Monte Carlo simulations are used to evaluate the performance of maximum likelihood estimators. Also, an application is performed comparing the detection results of the proposed model with Gaussian-, Gamma-, and Weibull-based regression models in synthetic aperture radar (SAR) images.

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  • 11.
    Palm, Bruna
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Bayer, Fabio M.
    Universidade Federal de Santa Maria, BRA.
    Cintra, Renato J.
    Universidade Federal Pernambuco, BRA.
    2-D Rayleigh autoregressive moving average model for SAR image modeling2022In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 171, article id 107453Article in journal (Refereed)
    Abstract [en]

    Two-dimensional (2-D) autoregressive moving average (ARMA) models are commonly applied to describe real-world image data, usually assuming Gaussian or symmetric noise. However, real-world data often present non-Gaussian signals, with asymmetrical distributions and strictly positive values. In particular, SAR images are known to be well characterized by the Rayleigh distribution. In this context, the ARMA model tailored for 2-D Rayleigh-distributed data is introduced—the 2-D RARMA model. The 2-D RARMA model is derived and conditional likelihood inferences are discussed. The proposed model was submitted to extensive Monte Carlo simulations to evaluate the performance of the conditional maximum likelihood estimators. Moreover, in the context of SAR image processing, two comprehensive numerical experiments were performed comparing anomaly detection and image modeling results of the proposed model with traditional 2-D ARMA models and competing methods in the literature. © 2022 The Authors

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  • 12.
    Palm, Bruna
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Bayer, Fabio M.
    Univ Fed Santa Maria, BRA.
    Machado, Renato
    Aeronaut Inst Technol ITA, BRA.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Vu, Viet Thuy
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Cintra, Renato J.
    Univ Fed Pernambuco, BRA.
    Robust Rayleigh Regression Method for SAR Image Processing in Presence of Outliers2022In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 60Article in journal (Refereed)
    Abstract [en]

    The presence of outliers (anomalous values) in synthetic aperture radar (SAR) data and the misspecification in statistical image models may result in inaccurate inferences. To avoid such issues, the Rayleigh regression model based on a robust estimation process is proposed as a more realistic approach to model this type of data. This article aims at obtaining Rayleigh regression model parameter estimators robust to the presence of outliers. The proposed approach considered the weighted maximum likelihood method and was submitted to numerical experiments using simulated and measured SAR images. Monte Carlo simulations were employed for the numerical assessment of the proposed robust estimator performance in finite signal lengths, their sensitivity to outliers, and the breakdown point. For instance, the nonrobust estimators show a relative bias value 65-fold larger than the results provided by the robust approach in corrupted signals. In terms of sensitivity analysis and break down point, the robust scheme resulted in a reduction of about 96% and 10%, respectively, in the mean absolute value of both measures, in compassion to the nonrobust estimators. Moreover, two SAR datasets were used to compare the ground type and anomaly detection results of the proposed robust scheme with competing methods in the literature. © 2022 IEEE.

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  • 13.
    Palm, Bruna
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Javadi, Saleh
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Bayer, Fabio M.
    Universidade Federal de Santa Maria, BRA.
    Vu, Viet Thuy
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Inflated Rayleigh Distribution for SAR Imagery Modeling2022In: International Geoscience and Remote Sensing Symposium (IGARSS 2022), Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 44-47Conference paper (Refereed)
    Abstract [en]

    Synthetic aperture radars (SAR) data plays an important role in remote sensing applications. It is common knowledge that SAR image amplitude pixels can be approximately modeled by the Rayleigh distribution. However, this model is contin-uous and does not accommodate points with non-zero prob-ability, such as a null pixel amplitude value. Thus, in this paper, we propose an inflated Rayleigh distribution for SAR image modeling that is based on a mixed continuous-discrete distribution and can be used to fit signals with observed values on [0, infty). The maximum likelihood approach is considered to estimate the parameters of the proposed distribution. An empirical experiment with a SAR image is also presented and discussed. © 2022 IEEE.

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  • 14.
    Palm, Bruna
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Javadi, Saleh
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Vu, Viet Thuy
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Sjogren, Thomas
    Swedish Defence Research Agency (FOI), Sweden.
    Wavelength Resolution SAR Change Detection: New Measurement Campaign for New Research Data Set2023In: ALGORITHMS FOR SYNTHETIC APERTURE RADAR IMAGERY XXX 2023 / [ed] Zelnio, E Garber, FD, SPIE - International Society for Optical Engineering, 2023, Vol. 12520, article id 1252009Conference paper (Refereed)
    Abstract [en]

    This paper describes a new measurement campaign for SAR images. The data consists of images collected by the Swedish LORA system associated with VHF-band (19-90 MHz). Due to the system frequency, detecting targets concealed in a forest is possible. Thus, this paper aims to share with the community the results of utilizing new VHF-band SAR data that allows the development of new methods for target and other change detection. In particular, to show the applicability of the new data set, a simple change detection method was performed to detect targets in a forest, resulting in 100% of detection, associated with no false alarm in a particular region of interest.

  • 15.
    Perez, Bibiana Gabardo
    et al.
    Univ Fed Santa Maria, BRA.
    Gaidarji, Bruna
    Univ Fed Santa Maria, BRA.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Ruiz-Lopez, Javier
    Univ Granada, ESP.
    Perez, Maria M.
    Univ Granada, ESP.
    Durand, Leticia Brandao
    Univ Fed Santa Maria, BRA.
    Masking ability of resin composites: Effect of the layering strategy and substrate color2022In: Journal of Esthetic and Restorative Dentistry, ISSN 1496-4155, E-ISSN 1708-8240, Vol. 34, no 8, p. 1206-1212Article in journal (Refereed)
    Abstract [en]

    Objective To evaluate the effect of layering strategy and substrate color on the masking ability of resin composites. Materials and Methods A1-shaded specimens from Charisma Diamond and Filtek Z350XT were produced using different layering strategies. Color measurements were made by a reflectance spectrophotometer over A2, C2, A3.5, C3, C4 substrates. Color differences were calculated and interpreted by the 50%:50% perceptibility and acceptability visual thresholds. Data was analyzed by Kruskal-Wallis and Dunn post hoc test. Chi-square test was used to determine the association between masking ability, and independent variables. Results Color differences were significantly lower on A2 and C2 in comparison with C4 for the majority of the layering strategies. Acceptable matches were observed on most of the combinations over A2. Moderately unacceptable mismatches were observed in most of the combinations over C2 and A3.5. Clearly unacceptable mismatches were observed on the C3 and C4. The Delta E-00 color shifts were predominantly influenced by Delta L-00 for all layering strategies and substrate colors. Conclusion Masking ability was affected by the layering strategy and substrate color. Acceptable masking was associated with A2 and C2, and with layering strategy composed of 0.5 mm enamel opacity and 1.0 mm dentin opacity thicknesses, using the Filtek Z350XT. Clinical Significance Resin composites-shade A1-applied by different layering strategies with a final thickness of 1.5 mm were able to mask mild and moderately discolored substrates. Severely discolored substrates were not masked effectively.

  • 16.
    Vinholi, João G.
    et al.
    Aeronautics Institute of Technology (ITA), São José dos Campos, BRA.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Silva, Danilo
    Federal University of Santa Catarina (UFSC), Florianopolis, BRA.
    Machado, Renato
    Aeronautics Institute of Technology (ITA), São José dos Campos, BRA.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Change Detection Based on Convolutional Neural Networks Using Stacks of Wavelength-Resolution Synthetic Aperture Radar Images2022In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 60, article id 5236414Article in journal (Refereed)
    Abstract [en]

    This article presents two supervised change detection algorithms (CDA) based on convolutional neural networks (CNN) that use stacks of co-registered wavelength-resolution synthetic aperture radar (SAR) images to detect changes in an image under monitoring. The additional information of a scene of interest provided by SAR image stacks can be explored to enhance the performance of change detection algorithms. In particular, stacks of images with similar statistics can be obtained for ultra-wideband (UWB) very high frequency (VHF) SAR systems, as they produce images highly stable in time. The proposed CDAs can be summed up into four stages: difference image formation, semantic segmentation, clustering, and change classification. The CNN-GSP algorithm is based on a ground scene prediction (GSP) image, which is used as a reference to form a difference image (DI). A CNN-based model then analyzes the DI. The CNN-MDI algorithm feeds multiple DIs with identical monitored images to a CNN-based model, which will concurrently analyze their features. Tests with CARABAS-II data show that the proposed CDAs can outperform other state-of-the-art algorithms that also use stacks of WR-SAR images. Beyond that, the proposed algorithms outperformed a CNN-based CDA that does not use image stacks, which shows that CNN-based algorithms can use the additional information provided by stacks of SAR images to reduce false alarm occurrences while increasing the probability of detection of changes. 

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  • 17.
    Vu, Viet Thuy
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Alves, Dimas
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Dammert, Patrik
    Saab, SWE.
    Hellsten, Hans
    Saab, SWE.
    A detector for wavelength resolution SAR incoherent change detection2019In: 2019 IEEE Radar Conference, RadarConf 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, article id 8835574Conference paper (Refereed)
    Abstract [en]

    This paper introduces an effective detector for wavelength-resolution SAR incoherent change detection. The detector is derived from Bayes' theorem. The input of the detector is the differences between surveillance and reference magnitude images simply obtained by a subtraction while the output is a summary of the detected changes. The proposed detector is tested with 24 CARABAS images that were obtained from the measurement campaign in northern Sweden in 2002. The testing results show that the detector can provide a high average detection probability, e.g., about 96%, with a very low false alarm rate, e.g., only 0.35 per square kilometer. © 2019 IEEE.

  • 18.
    Vu, Viet Thuy
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Palm, Bruna
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Alves, Dimas Irion
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences. Universidade Federal do Pampa, BRA.
    Gomes, Natanael Rodrigues
    Federal University of Santa Maria, BRA.
    Pettersson, Mats
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Wavelength-resolution SAR Change Detection with Changing Flight Heading during Passes2019In: 2019 International Radar Conference, RADAR 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, article id 9079155Conference paper (Refereed)
    Abstract [en]

    The paper proposes a solution for the problem of target angular observation and specular reflection of elongated structure in SAR change detection. To illuminate a certain target at different angles and/or to minimize specular reflection, a natural solution is to pass the ground scene several times using different flight tracks. This helps to increase the detection probability and reduce the false alarm rate simultaneously. To evaluate the proposal, we use a simple statistical hypothesis test derived from bivariate Rayleigh distribution and the CARABAS data. A comparison based on the detection probability and false alarm rate with other available SAR change detection method reveals the benefit of the proposal. © 2019 IEEE.

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