Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Rick Chartrand"'
Publikováno v:
IEEE Journal of Translational Engineering in Health and Medicine, Vol 2, Pp 1-18 (2014)
Exploiting sparsity in the image gradient magnitude has proved to be an effective means for reducing the sampling rate in the projection view angle in computed tomography (CT). Most of the image reconstruction algorithms, developed for this purpose,
Externí odkaz:
https://doaj.org/article/562b0e4491144aa09cb704336a4025c9
Publikováno v:
IGARSS
The global phenomenon of forest degradation is a pressing issue with severe implications for climate stability and biodiversity protection. In this work we generate Bayesian updating deforestation detection (BUDD) algorithms by incorporating Sentinel
Publikováno v:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI.
This paper presents a prototype crop production monitoring pipeline which identifies agricultural fields planted with small grains over 19 countries in the Middle East and North Africa (MENA) and monitors those crops over the growing season. The tech
Autor:
Scott Arko, Michael S. Warren, Matthew T. Calef, Caitlin Kontgis, Ryan Keisler, Rick Chartrand, Alice M. S. Durieux
Publikováno v:
Applications of Machine Learning.
Although monitoring forest disturbance is crucial to understanding atmospheric carbon accumulation and biodiversity loss, persistent cloud cover, especially in tropical areas, makes detecting forest disturbances using optical remotely sensed imagery
Publikováno v:
IGARSS
We consider the problem of unwrapping the phase of two-dimensional interferograms, and adopt a known formulation as a sparse optimization problem. Many algorithms have been developed for solving sparse optimization problems that occur in the field of
Publikováno v:
IGARSS
The European Space Agency’s Sentinel 1 satellite acquires global synthetic aperture radar (SAR) data, making it particularly well-suited for analyzing tropical regions that may be covered in clouds and therefore concealed from optical data. Here, w
Autor:
Rick Chartrand
Publikováno v:
IGARSS
We consider the problem of unwrapping the phase of synthetic aperture radar interferograms. Like several existing approaches, we use an estimate of the gradient of the interfer-ogram phase. Our contribution is to regularize this differentiation proce
Autor:
Carly Beneke, Mark M. Mathis, Daniela I. Moody, David Nicholaeff, Steven P. Brumby, Justin Poehnelt, Ryan Keisler, Michael S. Warren, Rick Chartrand, Samuel W. Skillman
Publikováno v:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV.
Autor:
Rick Chartrand
Publikováno v:
GlobalSIP
We consider the problem of differentiating a multivariable function specified by noisy data. Following previous work for the single-variable case, we regularize the differentiation process, by formulating it as an inverse problem with an integration
Publikováno v:
MultiTemp
Synthetic aperture radar (SAR) can penetrate clouds, rendering these data particularly useful for mapping land cover and land use in tropical areas. In this study, we leverage the image processing and analysis platform built at Descartes Labs to anal