A library of multivariate, high-dimensional statistics, and time series algorithms for spatial-temporal stacks.
Generation of geometric median pixel composite mosaics from a stack of data; see example.
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.
Accelerated generation of geometric median absolute deviation pixel composite mosaics from a stack of data; see example.
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).
see example.
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).
All advanced implementations and cutting-edge research codes are now found in github.com/daleroberts/hdstats-private. These are only available to research collaborators.