%0 Conference Proceedings
%4 sid.inpe.br/mtc-m21d/2021/07.12.17.24
%2 sid.inpe.br/mtc-m21d/2021/07.12.17.24.12
%T Assessment of nonlocal means stochastic distances speckle reduction for SAR time series
%D 2021
%A Doblas Prieto, Juan,
%A Frery, Alejandro C.,
%A Siqueira, Sant'Anna Sidnei Joćo,
%A Carneiro, Arian Ferreira,
%A Shimabukuro, Yosio Edemir,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Victoria University of Wellington
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress juandb@gmail.com
%@electronicmailaddress
%@electronicmailaddress sidnei.santanna@inpe.br
%@electronicmailaddress eng.ariancarneiro@gmail.com
%@electronicmailaddress edemirshima@gmail.com
%B International Geoscience and Remote Sensing Symposium (IGARSS)
%C Online
%8 12-16 July
%I IEEE
%J Breussels
%S Proceedings
%K Speckle filters, SAR timeseries, Quality, Assessment.
%X Implementation of complex SAR speckle filtering algorithms is usually limited to experimental settings, that make use of heavy-duty computers or processing clusters. Operational applications of SAR imagery, such as those used to map flashflooded areas, or to flag on-going deforestation, usually are not able to take advantage of these advanced filtering techniques. Here we introduce SDNLM3D, a fast and effective 3D filtering algorithm based on the non-local paradigm, suitable to be applied on cloud environments, such as the Google Earth Engine (GEE). A systematic, real-world based benchmark of more than 700 variations of the SDNLM3D revealed that the optimized version of the SDNLM3D filter outperforms the usual filters used operationally.
%@language en
%3 doblas_2021.pdf