The ENVI Hyperspectral Analysis of Minerals provides a foundational understanding of hyperspectral analysis, covering data preprocessing, advanced data reduction, and mapping&classification of mineral distribution. The 4 modules are designed for analysts who want to focus on extracting information from hyperspectral imagery for applications like material identification, mining and precision agriculture.
Core Components:
- Data Preprocessing: Learn about preparing hyperspectral imagery for geologic and mineral analysis. Produce a surface reflectance image that is ready for spectral analysis.
- Spectra Collection & Comparison: Learn how to plot and interpret image and reference (library) reflectance spectra. At some point during a hyperspectral image-processing workflow, you will be working with one or both types of spectra.
- Endmember Extraction: Learn different methods for identifying spectrally pure pixels in a hyperspectral image. These "endmembers" provide a starting point for locating unique and rare materials on the Earth's surface.
- Spectral Mapping Methods: Learn about different methods for mapping spectral endmembers in an image. Spectral mapping methods are frequently used in geologic applications such as mineral mapping.
This learning path will enable you to use hyperspectral imagery for mineralogical applications.
Learning outcomes
After this course, you will be able to:
- Apply image preprocessing tools to isolate spectral noise and generate high-fidelity surface reflectance images.
- Decode diagnostic mineral absorption features by comparing image-derived spectra against reference libraries.
- Master advanced dimensionality reduction techniques as MNF, Pixel Purity Index, N-Dimensional Visualizer, and SMACC.
- Execute spectral workflows for mapping precise sub-pixel abundancies and ranking mineral similarities.