USGS PRISM MICA
BREEZE GEO
Material Identification and Classification Algorithm Expert System based on publicly available USGS PRISM MICA expert system for material classification.
Parameters
Group
Group similar types of minerals with a predefined name and color
Default mineral group
Generalized mineral group
None
MICA File
Select up to 5 MICA mcf files
Pretreatments
In top-down applied order ⬇️ , meaning Savitzky-Golay is applied first then Derivative and so on.
Savitzky-Golay
Derivative
SNV (Standard Normal Variate)
Logarithm
Center
UV (Unit Variance) scaling
For more on each type of pretreatment see: Pretreatments
Threshold
Minimum score value required for classification of a specific class
Rule images
Calculates each pixel based on each class and see how well they compare to each other. The lower the value the more accurate the pixel spectra are to the class spectrum from the MICA file.
✅ Apply the rule images to all the classes in the library.
⬜ Rule images are not applied.
Show majority class
Show majority class category in table
✅
⬜
Save all statistics
Default saving fit value. With all statistics depth and weighted fit depth will also be saved in measurement prediction file
✅
⬜
Smooth
Smooth prediction result using median filter kernel
None
No Smoothing prediction.
Low
Smoothing using median filter kernel with 5x5 pixel box
Medium
Smoothing using median filter kernel with 10x10 pixel box
High
Smoothing using median filter kernel with 15x15 pixel box
Output
Type
Depicts the classified pixels in the table view.
Depth
Depicts how certain the model is in its classification. Values are between (0 - 1).
References
Clark, R. N. (1995). Mapping minerals, amorphous materials, environmental materials, vegetation, water, ice and snow, and other materials: The USGS tricorder algorithm. Clark, Roger N. SwaySummaries of the Fifth Annual JPL Airborne Earth Science Workshop. Volume 1: AVIRIS Workshopze, Gregg A., 39–40. https://ntrs.nasa.gov/search.jsp?R=19950027321
Clark, R. N. (2003). Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems. Journal of Geophysical Research, 108(E12), 5131. https://doi.org/10.1029/2002JE001847
Kokaly, R. F. (2011). PRISM: Processing Routines in IDL for Spectroscopic Measurements (Installation Manual and User’s Guide, Version 1.0=. In U.S. Geological Survey Open-File Report 2011-1155.
Kokaly, R. F., King, T. V. V., & Hoefen, T. M. (2011). Mapping the distribution of materials in hyperspectral data using the USGS Material Identification and Characterization Algorithm (MICA). International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.org/10.1109/IGARSS.2011.6049370
http://Pubs.usgs.gov/of/2011/1155/, & Kokaly, R. F. (2014). USGS PRISM http://pubs.usgs.gov/of/2011/1155/. https://pubs.usgs.gov/of/2011/1155/
Swayze, G. A., Clark, R. N., Goetz, A. F. H., Chrien, T. G., & Gorelick, N. S. (2003). Effects of spectrometer band pass, sampling, and signal‐to‐noise ratio on spectral identification using the Tetracorder algorithm. Journal of Geophysical Research, 108(E9), 5105. https://doi.org/10.1029/2002JE001975