194 citations · 509 across the 12 of their papers we have counts for
12 papers
Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target Imputation
Sara Björk, Stian N. Anfinsen, Michael Kampffmeyer +3
In prediction of forest parameters with data from remote sensing (RS), regression models have traditionally been trained on a small sample of ground reference data. This paper prop…
Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping
Jørgen A. Agersborg, Luigi T. Luppino, Stian Normann Anfinsen +1
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…
Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting
Vilde Jensen, Filippo Maria Bianchi, Stian Norman Anfinsen
This paper presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR). EnCQR constructs distribution-free and approximately margina…
Power Flow Balancing with Decentralized Graph Neural Networks
Jonas Berg Hansen, Stian Normann Anfinsen, Filippo Maria Bianchi
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,…
Guided Nonlocal Means Estimation of Polarimetric Covariance for Canopy State Classification
Jørgen A. Agersborg, Stian Normann Anfinsen, Jane Uhd Jepsen
We have developed a nonlocal algorithm for estimating polarimetric synthetic aperture radar (PolSAR) covariance matrices on single-look complex (SLC) format resolution. The algorit…
On the potential of sequential and non-sequential regression models for Sentinel-1-based biomass prediction in Tanzanian miombo forests
Sara Björk, Stian Normann Anfinsen, Erik Næsset +2
This study derives regression models for above-ground biomass (AGB) estimation in miombo woodlands of Tanzania that utilise the high availability and low cost of Sentinel-1 data. T…
Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images
Luigi T. Luppino, Mads A. Hansen, Michael Kampffmeyer +4
Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection in bitemporal satellite images. A main challenge is the align…
Polarimetric Guided Nonlocal Means Covariance Matrix Estimation for Defoliation Mapping
Jørgen A. Agersborg, Stian Normann Anfinsen, Jane Uhd Jepsen
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 e…
Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection
Luigi Tommaso Luppino, Michael Kampffmeyer, Filippo Maria Bianchi +4
Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection. Existing approaches train the networks by exploiting supe…
Unsupervised Image Regression for Heterogeneous Change Detection
Luigi T. Luppino, Filippo M. Bianchi, Gabriele Moser +1
Change detection 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…
Remote sensing image regression for heterogeneous change detection
Luigi T. Luppino, Filippo M. Bianchi, Gabriele Moser +1
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…
A clustering approach to heterogeneous change detection
Luigi Tommaso Luppino, Stian Normann Anfinsen, Gabriele Moser +4
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 co…