194 citations · 509 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…