Table Of Contents

Welcome to the PySptools documentation

Tools for hyperspectral imaging

This is the documentation at 2014-10-08.
stacked abundance maps

Hyperspectral imaging is used to visualize chemistry, the spatial relation between chemicals and the proportion of them. PySptools is a python module that implements spectral and hyperspectral algorithms. Specializations of the library are the endmembers extraction, unmixing process, supervised classification, target detection, noise reduction, convex hull removal and features extraction at spectrum level. The library is designed to be easy to use and almost all functionality has a plot function to save you time with the data analysis process. The actual sources of the algorithms are the Matlab Hyperspectral Toolbox of Isaac Gerg, the pwctools of M. A. Little, the Endmember Induction Algorithms toolbox (EIA) and the HySime Matlab module of José Bioucas-Dias and José Nascimento. You can download PySptools from the PySptools Project Page hosted by Sourceforge.net or from the pypi packages repository.

Project web site

Version 0.12.1 (beta)

Some fixes and ...

New functionalities

  • This version adds compatibility to the IPython Notebook. A display method is introduced for many classes. The display use matplotlib and have the same role than the plot method. Calling display show figures embedded in the Notebook.
  • The source is ported to Pyhton 3.3. This porting is not integral. Because the SPy library run on Python <= 2.7 and as the spectro module use SPy, the spectro module is not part of the porting.

New examples

Following examples use the IPython Notebook. You can look at three examples reworked for the Notebook:

And two new one using the Pine Creek cube:

Examples

A simple comparaison between ATGP and NFINDR.

Methanol gas synthetic images made by unmixing. The data used for the demonstration is acquired with a Telops Hyper-Cam instrument.

Quartz classification of a drill core datacube. The data used for the demonstration is acquired with a Telops Hyper-Cam instrument.

Smokestack effluents analysis. The data used for the demonstration is acquired with a Telops Hyper-Cam instrument.

Some examples of convex hull removal and features extraction.

Documentation

Download and installation

Introduction

Summary of functions and classes by modules

  1. abundance_maps (linear unmixing)
  • FCLS (function and class)
  • NNLS (function and class)
  • UCLS (function and class)
  1. classifiers (supervised)
  • NormXCorr (class)
  • SAM (class)
  • SID (class)
  1. detection
  • ACE (function and class)
  • CEM (function and class)
  • GLRT (function and class)
  • MatchedFilter (function and class)
  • OSP (function and class)
  1. distance
  • chebyshev (function)
  • NormXCorr (function)
  • SAM (function)
  • SID (function)
  1. eea (endmembers extraction algorithms)
  • ATGP (function and class)
  • FIPPI (function and class)
  • NFINDR (function and class)
  • PPI (function and class)
  1. formatting
  • convert2d (function)
  • convert3d (function)
  • normalize (function)
  1. material_count
  • HfcVd (function and class)
  • HySime (function and class)
  1. noise
  • Savitzky Golay filter (class)
  • MNF (class)
  • Whiten (function and class)
  1. sigproc
  • bilateral (function)
  1. spectro (Python 2.7 only)
  • convex_hull_removal (function)
  • FeaturesConvexHullQuotient (class)
  • SpectrumConvexHullQuotient (function with a class interface)
  • USGS06SpecLib (class)
  1. util
  • load_ENVI_file (function) (Python 2.7 only)
  • load_ENVI_spec_lib (function) (Python 2.7 only)
  • corr (function)
  • cov (function)

Glossary

Release notes

Links

Indices and tables