Contents Intro Configure Python Simulation Feature Extraction References Intro I have become sick of copy and pasting feature extraction code around so I’ve updated pytsg to include some basic feature extraction methods. To learn this new interface let’s discuss some common feature extraction methods for hyperspectral data, we are not going to touch on dimensionality reduction techniques like PCA as the interpretation is less obvious than scalar extraction techniques such as band ratios, polynomial fitting and gaussian decomposition.

Hi Everyone, I would like to make a short comment on drift of the wavelength features in hyperspectral instruments and cross instrument calibration. For the sake of my sanity and yours I’m only going to address wavelength position/alignment, spectral resolution is a slightly separate issue. The first point of clarification is that the wavelength standards have a dual purpose: to monitor the performance of the instrument and drift to allow you to correct the position of the spectral features to a known energy.

Logging stratigraphy on a drill rig to decide to continue or end the hole is a skill that every geologist should have. Ideally logging happens at the rig in realtime as we are often wanting to determine end of hole critera. Let’s consider how well hyperspectral and image data perform in classifing samples into prospective and non-prospective units. As usual we are going to use this dataset from from the C3DMM project.

Let’s talk about the performance of using chips vs hyperspectral to classify ore and waste in an Iron ore deposit. Before we go onto the technical work let’s discuss the practical uses of this information. Common use cases would be deciding if a sample is ore/waste, screening samples for further analysis or simply selecting the right sensor for you application and trading off cost/speed/performance/technical difficulty considerations. So with all that in mind I hope that you will find a smart application in your workplace.

Good Afternoon Spectroscopists, I though someone might be interested in using pytsg to process hyperspectral data. For this example we are going to use this dataset provided by CSIRO under a CC4 licence collected for the C3DMM project In this post we will use of PLS to predict Fe grade from the spectra. Let us start by installing the libraries that we are going to need. You can use the tsg file reader that I developed here or you can write your own…