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https://github.com/daleroberts/hdstats
A library of multivariate, high-dimensional statistics, and time series algorithms for spatial-temporal stacks.
https://github.com/daleroberts/hdstats
Last synced: 4 months ago
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A library of multivariate, high-dimensional statistics, and time series algorithms for spatial-temporal stacks.
- Host: GitHub
- URL: https://github.com/daleroberts/hdstats
- Owner: daleroberts
- License: bsd-3-clause
- Created: 2018-12-10T01:49:27.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-03-01T05:21:19.000Z (over 3 years ago)
- Last Synced: 2024-02-22T05:21:26.529Z (4 months ago)
- Language: Python
- Homepage:
- Size: 20.1 MB
- Stars: 17
- Watchers: 4
- Forks: 5
- Open Issues: 3
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Metadata Files:
- Readme: README.md
- License: LICENSE
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- awesome-stars - daleroberts/hdstats - A library of multivariate, high-dimensional statistics, and time series algorithms for spatial-temporal stacks. (Python)
README
# hdstats
A library of multivariate, high-dimensional statistics, and time series algorithms for spatial-temporal stacks.
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### Geometric median PCM
Generation of geometric median pixel composite mosaics from a stack of data; see [example](https://github.com/daleroberts/hdstats/blob/master/docs/geomedian.ipynb).
If you are using this algorithm in your research or products, please cite:
*Roberts, D., Mueller, N., & McIntyre, A. (2017). High-dimensional pixel composites from earth observation time series. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6254-6264.*
### Geometric Median Absolute Deviation (MAD) PCM
Accelerated generation of geometric median absolute deviation pixel composite mosaics from a stack of data; see [example](https://github.com/daleroberts/hdstats/blob/master/docs/mad.ipynb).
If you are using this algorithm in your research or products, please cite:
*Roberts, D., Dunn, B., & Mueller, N. (2018). Open data cube products using high-dimensional statistics of time series. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 8647-8650).*
### Feature generation for spatial-temporal time series stacks.
see [example](https://github.com/daleroberts/hdstats/blob/master/docs/temporal.ipynb).
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### Assumptions
We assume that the data stack dimensions are ordered so that the spatial dimensions are first (*y*,*x*), followed by the spectral dimension of size *p*, finishing with the temporal dimension. Algorithms reduce in the last dimension (typically, the temporal dimension).
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### Research and Development / Advanced Implementations
All advanced implementations and cutting-edge research codes are now found in [github.com/daleroberts/hdstats-private](https://github.com/daleroberts/hdstats-private). These are only available to research collaborators.