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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 different mcf files

Pretreatments

  • Savitzky-Golay

For more on each type of pretreatment see: Pretreatments

Threshold

Minimum score value required for classification of a specific class

Rule images

Calculate 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