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: 5 days 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 (almost 6 years ago)
• Default Branch: main
• Last Pushed: 2021-03-01T05:21:19.000Z (over 3 years ago)
• Last Synced: 2024-09-25T16:24:37.463Z (about 2 months ago)
• Language: Python
• Homepage:
• Size: 20.1 MB
• Stars: 17
• Watchers: 4
• Forks: 5
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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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.