Blar i forfatter Artikler, rapporter og annet (fysikk og teknologi) "Salberg, Arnt Børre"

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    • Deep divergence-based approach to clustering 

      Kampffmeyer, Michael C.; Løkse, Sigurd; Bianchi, Filippo Maria; Livi, Lorenzo; Salberg, Arnt Børre; Jenssen, Robert (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-02-08)
      A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function. As opposed to supervised deep learning, this line of research is in its infancy, and how to design and optimize suitable loss functions to train deep neural networks for clustering is still an open question. ...

    • Dense dilated convolutions merging network for land cover classification 

      Liu, Qinghui; Kampffmeyer, Michael; Jenssen, Robert; Salberg, Arnt Børre (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-03-06)
      Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotations, and an inherent complexity in the semantic segmentation task. In this article, we propose a novel architecture called the dense dilated convolutions' merging network (DDCM-Net) to address this task. The proposed ...

    • Large-Scale Mapping of Small Roads in Lidar Images Using Deep Convolutional Neural Networks 

      Salberg, Arnt Børre; Trier, Øivind Due; Kampffmeyer, Michael C. (Chapter; Bokkapittel, 2017-05-19)
      Detailed and complete mapping of forest roads is important for the forest industry since they are used for timber transport by trucks with long trailers. This paper proposes a new automatic method for large-scale mapping forest roads from airborne laser scanning data. The method is based on a fully convolutional neural network that performs end-to-end segmentation. To train the network, a large set ...

    • Multi-View Self-Constructing Graph Convolutional Networks With Adaptive Class Weighting Loss for Semantic Segmentation 

      Liu, Qinghui; Kampffmeyer, Michael; Jenssen, Robert; Salberg, Arnt Børre (Conference object; Konferansebidrag, 2020-07-28)
      We propose a novel architecture called the Multi-view Self-Constructing Graph Convolutional Networks (MSCG-Net) for semantic segmentation. Building on the recently proposed Self-Constructing Graph (SCG) module, which makes use of learnable latent variables to self-construct the underlying graphs directly from the input features without relying on manually built prior knowledge graphs, we leverage ...

    • Oil spill characterization in the hybrid-polarity SAR domain using log-cumulants 

      Espeseth, Martine; Skrunes, Stine; Brekke, Camilla; Salberg, Arnt Børre; Jones, Cathleen; Holt, Benjamin (Peer reviewed; Journal article; Tidsskriftsartikkel, 2016-10-18)
      Log-cumulants have proven to be an interesting tool for evaluating the statistical properties of potential oil spills in polarimetric Synthetic Aperture Radar (SAR) data within the common horizontal (H) and vertical (V) polarization basis. The use of first, second, and third order sample log-cumulants has shown potential for evaluating the texture and the statistical distributions, as well as ...

    • Self-constructing graph neural networks to model long-range pixel dependencies for semantic segmentation of remote sensing images 

      Liu, Qinghui; Kampffmeyer, Michael; Jenssen, Robert; Salberg, Arnt Børre (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-06-16)
      Capturing global contextual representations in remote sensing images by exploiting long-range pixel-pixel dependencies has been shown to improve segmentation performance. However, how to do this efficiently is an open question as current approaches of utilising attention schemes, or very deep models to increase the field of view, increases complexity and memory consumption. Inspired by recent work ...

    • Semi-supervised target classification in multi-frequency echosounder data 

      Choi, Changkyu; Kampffmeyer, Michael; Handegard, Nils Olav; Salberg, Arnt Børre; Brautaset, Olav; Eikvil, Line; Jenssen, Robert (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-08-12)
      Acoustic target classification in multi-frequency echosounder data is a major interest for the marine ecosystem and fishery management since it can potentially estimate the abundance or biomass of the species. A key problem of current methods is the heavy dependence on the manual categorization of data samples. As a solution, we propose a novel semi-supervised deep learning method leveraging a few ...

    • Urban land cover classification with missing data modalities using deep convolutional neural networks 

      Kampffmeyer, Michael C.; Salberg, Arnt Børre; Jenssen, Robert (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-06-14)
      Automatic urban land cover classification is a fundamental problem in remote sensing, e.g., for environmental monitoring. The problem is highly challenging, as classes generally have high interclass and low intraclass variances. Techniques to improve urban land cover classification performance in remote sensing include fusion of data from different sensors with different data modalities. However, ...