LAST NEWS

LAST NEWS FROM SONDRA

(Thread) And the winners of the @sentinel_hub

(Thread) And the winners of the @sentinel_hub Custon Script Contest are: First place for Thomas di Martino et al. for their "Reactiv Script" featuring #SAR change detection. Find the script here : 🔴 (Thread) And the winners🎉 of the @sentinel_hub Custon Script Contest are: First place for Thomas di Martino et al. for their "Reactiv Script" featuring #SAR change detection. Find the script here ➡️ https://t.co/Vdi1MEVmbz #scicomm #OpenData 1/n pic.twitter.com/yrt6e8sQSb— Sentinel Hub (@sentinel_hub) January 5, 2021

Par |février 15th, 2021|Catégories : News|0 Comments
  • 3709

Robust Low-rank Change Detection for Multivariate SAR Image Time Series

juin 3rd, 2020|0 Comments

Home  I  Latest News  I  Testimonials  I  Blog  I  Our Team  I  About us  I  Jobs Offer  I  Contact us

By Ammar Mian, Antoine Collas, Arnaud Breloy Guillaume Ginolhac and Jean-Philippe Ovarlez This paper derives a new change detector for multivariate Synthetic Aperture Radar image time series. Classical statistical change detection methodologies based on covariance matrix analysis are usually built upon the Gaussian assumption, as well as an unstructured signal model. Both of these hypotheses may be inaccurate for high-dimension/resolution images, where the noise can be heterogeneous (non-Gaussian) and where the relevant signals usually lie in a low dimensional subspace (low-rank structure). These two issues are tackled by proposing a new Generalized Likelihood Ratio Test based on a robust (compound Gaussian) low-rank (structured covariance matrix) model. The interest of the proposed detector is assessed on two Synthetic Aperture Radar Image Time Series data set from UAVSAR. Read the full article here
Load more posts