Blar i forfatter "Anfinsen, Stian Normann"

Viser treff 21-35 av 35

    • Polarimetric Guided Nonlocal Means Covariance Matrix Estimation for Defoliation Mapping 

      Agersborg, Jørgen Andreas; Anfinsen, Stian Normann; Jepsen, Jane Uhd (Conference object; Konferansebidrag, 2020)
      In this study we investigate the potential for using synthetic aperture radar (SAR) data to provide high resolution defoliation and regrowth mapping of trees in the tundra-forest ecotone. Using aerial photographs, four areas with live forest and four areas with dead trees were identified. Quad-polarimetric SAR data from RADARSAT-2 was collected from the same area, and the complex multilook polarimetric ...

    • Polarimetric SAR Change Detection with the Complex Hotelling-Lawley Trace Statistic 

      Akbari, Vahid; Anfinsen, Stian Normann; Doulgeris, Anthony Paul; Eltoft, Torbjørn; Moser, Gabriele; Serpico, Sebastian Bruno (Journal article; Tidsskriftartikkel; Peer reviewed, 2016-03-15)
      In this paper, we propose a new test statistic for unsupervised change detection in polarimetric radar images. We work with multilook complex covariance matrix data, whose underlying model is assumed to be the scaled complex Wishart distribution. We use the complex-kind Hotelling-Lawley trace statistic for measuring the similarity of two covariance matrices. The distribution of the Hotelling-Lawley ...

    • Power Flow Balancing With Decentralized Graph Neural Networks 

      Hansen, Jonas Berg; Anfinsen, Stian Normann; Bianchi, Filippo Maria (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-08-01)
      We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in energy grids. The balancing is framed as a supervised vertex regression task, where the GNN is trained to predict the current and power injections at each grid branch that yield a power flow balance. By representing the power grid as a line graph with branches as vertices, we can train a GNN that ...

    • Probability distributions for wind speed volatility characteristics: A case study of Northern Norway 

      Chen, Hao; Anfinsen, Stian Normann; Birkelund, Yngve; Yuan, Fuqing (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11)
      The Norwegian Arctic is rich in wind resources. The development of wind power in this region can boost green energy and also promote local economies. In wind power engineering, it is a tremendous advantage to base projects on a sound understanding of the intrinsic properties of wind resources in an area. Wind speed volatility, a phenomenon that strongly affects wind power generation, has not received ...

    • Radar size inference from statistics of RCS samples 

      Anfinsen, Stian Normann; Grydeland, Tom; Vierinen, Juha; Kastinen, Daniel; Ricker, Robert; Arntzen, Ingar M; Kero, J.; Høgda, Kjell Arild (Journal article; Tidsskriftartikkel; Peer reviewed, 2023)
      Radar Beam-park experiments have been very successful in characterizing the distribution of space debris objects, both in terms of orbital parameters but also in terms of limiting the estimates of their radar cross section, or RCS. A recent paper \[0\] used observed range and range rates to refine orbit estimates by matching up the observed SNR curve to that predicted by simulations. This gives good ...

    • Remote sensing image regression for heterogeneous change detection 

      Luppino, Luigi Tommaso; Bianchi, Filippo Maria; Moser, Gabriele; Anfinsen, Stian Normann (Conference object; Konferansebidrag, 2018-11-01)
      Change detection in heterogeneous multitemporal satellite images is an emerging topic in remote sensing. In this paper we propose a framework, based on image regression, to perform change detection in heterogeneous multitemporal satellite images, which has become a main topic in remote sensing. Our method learns a transformation to map the first image to the domain of the other image, and vice versa. ...

    • A revisit of the Gram-Charlier and Edgeworth series expansions 

      Brenn, Torgeir; Anfinsen, Stian Normann (Journal article; Tidsskriftartikkel, 2017-07-31)
      In this paper we make several observations on the Gram- Charlier and Edgeworth series, which are methods for modeling and approximating probability density functions.We present a simplified derivation which highlights both the similarity and the differences of the series expansions, that are often obscured by alternative derivations. We also introduce a reformulation of the Edgeworth series ...

    • Statistical Analysis of Multilook Polarimetric Radar Images with the Mellin Transform 

      Anfinsen, Stian Normann (Doctoral thesis; Doktorgradsavhandling, 2010-05-19)
      This thesis presents methods for statistical analysis of the probability distributions used to model multilook polarimetric radar images. The methods are based on a matrix-variate version of Mellin's integral transform. The proposed theoretical framework is referred to as Mellin kind statistics. It is an extension of a theory recently developed for single polarisation amplitude and intensity data ...

    • Statistical Unmixing of SAR Images 

      Anfinsen, Stian Normann (Journal article; Tidsskriftartikkel, 2016-02-03)
      A method is presented which uses logarithmic statistics to detect and characterise class mixtures and targets in background clutter in synthetic aperture radar (SAR) images. Mixtures of ground cover types show up as extreme radar texture in statistical analysis of SAR images. Instead of modelling this as a spatially nonstationary radar cross section, this paper demonstrates how a mixture model ...

    • Subband Extraction Strategies in Ship Detection With the Subaperture Cross-Correlation Magnitude 

      Brekke, Camilla; Anfinsen, Stian Normann; Larsen, Yngvar (Journal article; Tidsskriftartikkel; Peer reviewed, 2012-01-27)
      The subaperture cross-correlation magnitude (SCM) has previously been proposed as a statistic that improves the contrast between small ship targets and the surrounding sea in synthetic-aperture-radar images. This preprocessing technique utilizes the fast decorrelation of open-water surface ripples on the scale of the SAR wavelength relative to coherent targets such as a ship. However, optimization ...

    • A Textural–Contextual Model for Unsupervised Segmentation of Multipolarization Synthetic Aperture Radar Images 

      Akbari, Vahid; Doulgeris, Anthony Paul; Gabriele, Moser; Eltoft, Torbjørn; Sebastiano, B. Serpico; Anfinsen, Stian Normann (Journal article; Tidsskriftartikkel; Peer reviewed, 2013)
      This paper proposes a novel unsupervised, non-Gaussian, and contextual segmentation method that combines an advanced statistical distribution with spatial contextual informa-tion for multilook polarimetric synthetic aperture radar (PolSAR)data. This extends on previous studies that have shown the added value of both non-Gaussian modeling and contextual smoothing individually or for intensity channels ...

    • Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping 

      Agersborg, Jørgen Andreas; Luppino, Luigi Tommaso; Anfinsen, Stian Normann; Jepsen, Jane Uhd (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-10-20)
      Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not well-suited to detect weak signatures of certain disturbances of ecological systems. To resolve this problem we propose a new approach based on image-to-image translation and one-class ...

    • Unsupervised Estimation of the Equivalent Number of Looks in PolSAR Image with High Heterogeneity 

      Hu, Dingsheng; Qiu, Xiaolan; Anfinsen, Stian Normann; Lei, Bin (Journal article; Tidsskriftartikkel; Peer reviewed, 2017-03-01)
      Equivalent Number of Looks (ENL) is an important parameter in statistical modelling of multi-look Polarimetric SAR (PolSAR) data. In some automated applications of PolSAR images, it is necessary to estimate the ENL in an unsupervised way without any manual intervention. The existing unsupervised estimation of ENL can not obtain accurate estimates for the images with high heterogeneity. To address ...

    • Unsupervised Image Regression for Heterogeneous Change Detection 

      Luppino, Luigi Tommaso; Bianchi, Filippo Maria; Moser, Gabriele; Anfinsen, Stian Normann (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-08-14)
      Change detection (CD) in heterogeneous multitemporal satellite images is an emerging and challenging topic in remote sensing. In particular, one of the main challenges is to tackle the problem in an unsupervised manner. In this paper, we propose an unsupervised framework for bitemporal heterogeneous CD based on the comparison of affinity matrices and image regression. First, our method quantifies ...

    • Unsupervised Mixture-Eliminating Estimation of Equivalent Number of Looks for PolSAR Data 

      Hu, Dingsheng; Anfinsen, Stian Normann; Qiu, X; Doulgeris, Anthony Paul; Lei, Bin (Journal article; Tidsskriftartikkel; Peer reviewed, 2017-08-22)
      This paper addresses the impact of mixtures between classes on equivalent number of looks (ENL) estimation. We propose an unsupervised ENL estimator for polarimetric synthetic aperture radar (PolSAR) data, which is based on small sample estimates but incorporates a mixture-eliminating (ME) procedure to automatically assess the uniformity of the estimation windows. A statistical feature derived from ...