English
norskBlar i forfatter "Anfinsen, Stian Normann"
Viser treff 1-20 av 35
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Assessing polarimetric SAR sea-ice classifications using consecutive day images
(Journal article; Tidsskriftartikkel; Peer reviewed, 2015)This paper investigates automatic segmentation and classification of C-band, polarimetric synthetic aperture radar (SAR) satellite images of Arctic sea ice under freezing conditions prior to melt. The objective is to investigate the robustness of the results obtained under slightly varying environmental conditions and different viewing geometries. Initially, three geographically overlapping SAR ... -
Attention-guided Temporal Convolutional Network for Non-intrusive Load Monitoring
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-12-01)With the prevalence of smart meter infrastructure, data analysis on consumer side becomes more and more important in smart grid systems. One of the fundamental tasks is to disaggregate users' total consumption into appliance-wise values. It has been well noted that encoding of temporal dependency is a key issue for successful modelling of the relations between the total consumption and its decomposed ... -
Change Detection with Heterogeneous Remote Sensing Data: From Semi-Parametric Regression to Deep Learning
(Conference object; Konferansebidrag, 2020) -
A change detector for polarimetric SAR data based on the relaxed Wishart distribution
(Journal article; Tidsskriftartikkel; Peer reviewed, 2015-11-12)In this paper, we present an unsupervised change detection method for polarimetric synthetic aperture radar (PolSAR) images based on the relaxed Wishart distribution. Most polarimetric change detectors assume the Gaussian-based complex Wishart model for multilook covariance matrices, which is only satisfied for homogeneous areas with fully developed speckle and no texture. Liu et al. recently proposed ... -
A clustering approach to heterogeneous change detection
(Chapter; Bokkapittel, 2017-05-19)Change detection in heterogeneous multitemporal satellite images is a challenging and still not much studied topic in remote sensing and earth observation. This paper focuses on comparison of image pairs covering the same geographical area and acquired by two different sensors, one optical radiometer and one synthetic aperture radar, at two different times. We propose a clustering-based technique ... -
Comparative study of data-driven short-term wind power forecasting approaches for the Norwegian Arctic region
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-04-26)This paper conducts a systemic comparative study on univariate and multivariate wind power forecasting for five wind farms inside the Arctic area. The development of wind power in the Arctic can help reduce greenhouse gas emissions in this environmentally fragile region. In practice, wind power forecasting is essential to maintain the grid balance and optimize electricity generation. This study first ... -
A comparative study of sea clutter covariance matrix estimators
(Journal article; Tidsskriftartikkel; Peer reviewed, 2013-10-23)Estimation of the polarimetric covariance matrix is an important task in statistical modeling of sea clutter for maritime applications of polarimetric synthetic aperture radar (PolSAR) data. This work provides a comprehensive study of four covariance matrix estimators: the maximum likelihood estimators under the Gaussian distribution (G-ML) and the K distribution (K-ML), an approximation of the ... -
Comparison of feature based segmentation of full polarimetric SAR satellite sea ice images with manually drawn ice charts
(Journal article; Tidsskriftartikkel; Peer reviewed, 2013)In this paper we investigate the performance of an algorithm for automatic segmentation of full polarimetric, synthetic aperture radar (SAR) sea ice scenes. The algorithm uses statistical and polarimetric properties of the backscattered radar signals to segment the SAR image into a specified number of classes. This number was determined in advance from visual inspection of the SAR image and by ... -
Deep Image Translation With an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-17)Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection. Existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. A main challenge in the unsupervised problem setting is to avoid that change pixels affect the learning of the translation function. We propose ... -
Delt situasjonsforståelse under søk og redning i nordområdene
(Research report; Forskningsrapport, 2016-05-03)Prosjektets mål er å bidra til økt kunnskap om hvordan etablere delt situasjonsforståelse mellom sentrale aktører innen SAR i Nordområdet. Prosjektgruppa har arbeidet ut ifra en menneske-teknologi-organisasjon (MTO) tilnærming og hvor vi har sett på <br>1) menneskelige og organisatoriske faktorer og <br>2) tekniske faktorer som virker inn på etableringen av delt situasjonsforståelse mellom aktører.<br> ... -
A detection theoretical approach to digital communications using autoregressive process shift keying
(Master thesis; Mastergradsoppgave, 2001-03-10)I klassisk digital kommunikasjon overføres en bitstrøm gjennom en kanal ved å modulere parametrene til en deterministisk bærebølge. Noen kjente eksempler er amplitudemodulasjon (AM), frekvensmodulasjon (FM) og fasemodulasjon (PM). Mottakeren estimerer parametrene til det informasjonsbærende signalet og bruker en deteksjonsregel til å klassifisere den mottatte bølgeformen som en av flere mulige ... -
Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting
(Journal article; Tidsskriftartikkel, 2022-11-04)This article presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR). EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), which are suitable for nonstationary and heteroscedastic time series data. EnCQR can be applied on top of a generic forecasting model, including deep learning architectures. EnCQR ... -
A Framework for Mellin Kind Series Expansion Methods
(Journal article; Tidsskriftartikkel, 2017-07-31)Mellin kind statistics (MKS) is the framework which arises if the Fourier transform is replaced with the Mellin transform when computing the characteristic function from the probability density function. We may then proceed to retrieve logarithmic moments and cumulants, that have important applications in the analysis of heavy-tailed distribution models for nonnegative random variables. In this paper ... -
Generation of Lidar-Predicted Forest Biomass Maps from Radar Backscatter with Conditional Generative Adversarial Networks
(Conference object; Konferansebidrag, 2020) -
Heterogeneous Change Detection with Self-supervised Deep Canonically Correlated Autoencoders
(Conference object; Konferansebidrag, 2020) -
The Hotelling-Lawley trace statistic for change detection in polarimetric SAR data under the complex Wishart distribution
(Journal article; Tidsskriftartikkel; Peer reviewed, 2014-01-27)In this paper we propose a new test statistic for unsupervised changedetectioninpolarimetricsyntheticapertureradar(PolSAR) data. We work with multilook complex (MLC) covariance matrix data, whose underlying model is assumed to be the scaled complex Wishart distribution. We use the complex kind Hotelling-Lawley (HL) trace statistic for measuring the similarity of two covariance matrices. The sampling ... -
A K-Wishart Markov random field model for clustering of polarimetric SAR imagery
(Peer reviewed; Bokkapittel; Bok; Book; Chapter, 2011-10-20)A clustering method that combines an advanced statistical distribution with spatial contextual information is proposed for multilook polarimetric synthetic aperture radar (PolSAR) data. It is based on a Markov random field (MRF) model that integrates a K-Wishart distribution for the PolSAR data statistics conditioned to each image cluster and a Potts model for the spatial context. Specifically, the ... -
A K-Wishart Markov random field model for clustering of polarimetric SAR imagery
(Conference object; Konferansebidrag, 2011) -
A Multitexture Model for Multilook Polarimetric Synthetic Aperture Radar Data
(Journal article; Tidsskriftartikkel; Peer reviewed, 2013)A statistical model for multilook polarimetric radar data is presented where the polarimetric channels are associated with individual texture variables having potentially different statistical properties. The feasibility of producing closed form probability density functions under certain restrictions is outlined. Mellin kind statistics are derived under various assumptions on the texture variables, ... -
On the Potential of Sequential and Nonsequential Regression Models for Sentinel-1-Based Biomass Prediction in Tanzanian Miombo Forests
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-06-03)This study derives regression models for aboveground biomass (AGB) estimation in miombo woodlands of Tanzania that utilize the high availability and low cost of Sentinel-1 data. The limited forest canopy penetration of C-band SAR sensors along with the sparseness of available ground truth restricts their usefulness in traditional AGB regression models. Therefore, we propose to use AGB predictions ...
