Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Olsen, Peder A."'
With the increasing deployment of earth observation satellite constellations, the downlink (satellite-to-ground) capacity often limits the freshness, quality, and coverage of the imagery data available to applications on the ground. To overcome the d
Externí odkaz:
http://arxiv.org/abs/2403.11434
This paper presents a neural-network-based solution to recover pixels occluded by clouds in satellite images. We leverage radio frequency (RF) signals in the ultra/super-high frequency band that penetrate clouds to help reconstruct the occluded regio
Externí odkaz:
http://arxiv.org/abs/2106.08408
Phenotyping is the process of measuring an organism's observable traits. Manual phenotyping of crops is a labor-intensive, time-consuming, costly, and error prone process. Accurate, automated, high-throughput phenotyping can relieve a huge burden in
Externí odkaz:
http://arxiv.org/abs/1905.13291
Research in neural networks in the field of computer vision has achieved remarkable accuracy for point estimation. However, the uncertainty in the estimation is rarely addressed. Uncertainty quantification accompanied by point estimation can lead to
Externí odkaz:
http://arxiv.org/abs/1903.07427
In this paper, we propose a new method called ProfWeight for transferring information from a pre-trained deep neural network that has a high test accuracy to a simpler interpretable model or a very shallow network of low complexity and a priori low t
Externí odkaz:
http://arxiv.org/abs/1807.07506
We consider the problem of removing and replacing clouds in satellite image sequences, which has a wide range of applications in remote sensing. Our approach first detects and removes the cloud-contaminated part of the image sequences. It then recove
Externí odkaz:
http://arxiv.org/abs/1604.03915
We introduce a new convex formulation for stable principal component pursuit (SPCP) to decompose noisy signals into low-rank and sparse representations. For numerical solutions of our SPCP formulation, we first develop a convex variational framework
Externí odkaz:
http://arxiv.org/abs/1406.1089
Publikováno v:
Journal of Computational and Graphical Statistics, 2001 Mar 01. 10(1), 158-184.
Externí odkaz:
https://www.jstor.org/stable/1391032
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.