ID
- hyperquest.intrinsic_dimensionality.random_matrix_theory(path_to_data, noise_variance, mask_waterbodies=True, alpha=0.5, no_data_value=-9999)
A line-by-line python adaption of K. Cawse-Nicholson algorithm presented in,
Cawse-Nicholson, K., Raiho, A. M., Thompson, D. R., Hulley, G. C., Miller, C. E., Miner, K. R., … & Zareh, S. K. (2022). Intrinsic dimensionality as a metric for the impact of mission design parameters. Journal of Geophysical Research: Biogeosciences, 127(8), e2022JG006876.
- Parameters:
path_to_data (str) – Path to the .hdr or .nc
noise_variance (ndarray) – Noise variance for each band. Used to compute N (noise covariance matrix of size bands x bands).
mask_waterbodies (bool, optional) – Whether to mask water bodies based on NDWI threshold of 0.25. Default is True.
alpha (float) – Significance level. 0.5 was found to be optimal in a study using hyperspectral data (Cawse-Nicholson et al., 2012).
no_data_value (int or float) – Value used to describe no data regions.
- Returns:
K_est – Intrinsic Dimensionality (ID) of image.
- Return type: