Cres Module¶
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class
spectral_libraries.core.cres.Cres[source]¶ Bases:
objectConstrained Reference Endmember Selection. Unmix a given signal with all models in a library. Then create an index to see which models are a close match to estimated fractions.
Citations:
Roberts, D.A., Smith, M.O., Adams, J.B., 1993, Green vegetation, non-photosynthetic vegetation and soils in AVIRIS data, Remote Sensing of Environment, 44, p. 255-270.
Roberts, D.A., Batista, G., Pereira, J., Waller, E., and Nelson, B., 1999, Change Identification using Multitemporal Spectral Mixture Analysis: Applications in Eastern Amazonia, Remote Sensing Change Detection: Environmental Monitoring Methods and Applications, Edited by Elvidge, C. and Lunetta R., Chapter 9, p. 137-161.
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execute(signal: numpy.array, library: numpy.array, classes: list, spectrum_names: list, rmse_constraint: float = 0.025, shade: numpy.array = array([], dtype=float64))[source]¶ Unmix a given signal with a library and create fractions and RMSE values for each endmember in that library.
Parameters: - signal – The signal to unmix, without bad bands and scaled to reflectance values.
- library – The library used for unmixing, without bad bands and scaled to reflectance values.
- classes – The class of each spectrum in the library (e.g. GV, SOIL, …) [list-of-str]
- spectrum_names – The name of each spectrum in the library [list-of-str]
- rmse_constraint – Only keep models that fulfill this constraint.
- shade – Shade-spectrum in case of non-photometric shade.
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get_fractions() → list[source]¶ Get the calculated fractions (and RMSE) as a list of dictionaries, specifically designed for a QTableModel. In the list, each dictionary represents one endmember combination from the library (=one model). Assuming we have n classes in the library, each model’s dictionary has n key-value pairs with the spectra names, n key-value pairs with the fractions, one key-value pair with the shade fraction and one key-value pair with the RMSE.
The dictionary keys match the table header names.
Example for gv-soil mixture:
{gv_name: tree1, soil_name: clay4, gv_fraction: 0.25, soil_fraction: 0.55, shade_fraction: 0.20, rmse: 0.01}
Returns: A list of dictionaries, one per model, containing the spectra names, fractions and RMSE.
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get_header() → list[source]¶ Get the header for the QTableModel. It has header names for the spectra names, fractions and RMSE. The header names match the keys in the table dictionaries.
Example for gv-soil mixture:
['gv_name', 'soil_name', 'gv_fraction', 'soil_fraction', 'shade_fraction', 'RMSE']
Returns: The header for the GUI QTableModel.
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index(target_fractions: list, weight_per_target: list, weight_rmse: int) → dict[source]¶ Calculate the CRES indices based on input from the user.
Example for GV-SOIL-NPV mixture:
GV index = rmse weight * rmse + |GV calculated - estimated fraction| * GV weight + |SOIL calculated - estimated fraction| + |NPV calculated - estimated fraction| + |Shade calculated - estimated fraction|
Parameters: - target_fractions – The estimated fractions of the spectrum that is being unmixed (list-of-floats).
- weight_per_target – The weights per class for calculating the indices (list-of-ints between 1 and 10).
- weight_rmse – The weight attached to the RMSE for calculating the indices (int between 1 and 10).
Returns: Indices for each endmember and each class. The unique classes from the library metadata are the keys.
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