Sea-Ice Data Content Representation Based on Latent Dirichlet Allocation for Belgica Bank in Greenland

Data description File type : -.npy (python numpy file) 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...

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Bibliographic Details
Main Authors: Karmakar Chandrabali, Dumitru Octavian, Datcu Mihai
Format: Dataset
Language:English
Published: 2021
Subjects:
Latent Dirichlet Allocation
Topics
Sentinel-1
Belgica Bank
Greenland
The Sentinel
Sea ice
Online Access:https://zenodo.org/record/5075861
https://doi.org/10.5281/zenodo.5075861
Description
Summary:Data description File type : -.npy (python numpy file) 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]. Software to open with: Python Example code: import numpy Data= numpy.load(“filename_with_path”) Reference: 1. C. Karmakar, C.O. Dumitru, G. Schwarz, and M. Datcu, “Feature-Free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation”, IEEE JSTARS, vol. 14, pp. 676-689, 2021.

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