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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| Format: | Dataset |
| Language: | English |
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Zenodo
2021
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.5281/zenodo.5075860 https://zenodo.org/record/5075860 |
| 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. 2. C. Karmakar, C.O. Dumitru, and M. Datcu, “Explainable AI for SAR Image Time Series: Knowledge Extraction for Polar Areas”, MDPI Remote Sensing Journal, 2021, pp. 1-21 (under review). |
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