5075861
doi
10.5281/zenodo.5075861
oai:zenodo.org:5075861
user-polarops
user-eu
Dumitru Octavian
DLR
Datcu Mihai
DLR
Sea-Ice Data Content Representation Based on Latent Dirichlet Allocation for Belgica Bank in Greenland
Karmakar Chandrabali
DLR
doi:10.1109/JSTARS.2020.3039012
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Latent Dirichlet Allocation, Topics, Sentinel-1
<p><strong>Data description</strong></p>
<p>File type : -.npy (python numpy file)</p>
<p>File content: Each file is a numpy array of size (number of 256x256 patches, 4096) indexed by id of the patch (each scene contains 6,400 patches, each patch has 4,096 micropatches of size 4x4, assigned one topic [1] per micropatch, resulting in 4,096 topics per patch). Each file has 4 months of observation. Array size is 25600 x 4096. We provide 6 files containing 24 months of observation (see the excel file for the Sentinel-1 ids) [2].</p>
<p>Software to open with: Python</p>
<p>Example code:</p>
<p>import numpy</p>
<p>Data= numpy.load(“filename_with_path”)</p>
<p> </p>
<p>Reference:</p>
<p>1. C. Karmakar, C.O. Dumitru, G. Schwarz, and M. Datcu, “<em>Feature-Free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation</em>”, IEEE JSTARS, vol. 14, pp. 676-689, 2021.</p>
Zenodo
2021-07-06
info:eu-repo/semantics/other
5075860
user-polarops
user-eu
award_title=From Copernicus Big Data to Extreme Earth Analytics; award_number=825258; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/825258; funder_id=00k4n6c32; funder_name=European Commission;
1676633715.38225
244707586
md5:e0ed6b73b52cd738e39bd92bda029986
https://zenodo.org/records/5075861/files/TopicRepresentation_4x4_pixels-2018_2019.zip
public
10.1109/JSTARS.2020.3039012
Is cited by
doi
10.5281/zenodo.5075860
isVersionOf
doi