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# Welcome to the PySptools documentation

Tools for hyperspectral imaging

    Documentation at 2015-01-15.

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

## New for version 0.13.0 (beta)

- The class classification.SVC adapt the scikit-learn implementation of Support Vector Machines to the analysis of hyperspectral cubes.
- An example of using the SVC class with the Pine Creek cube.

## Examples

A simple comparaison between ATGP and NFINDR.

NFINDR and ATGP

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

Methanol gas

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

Hematite drill core

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

Smokestack

Some examples of convex hull removal and features extraction.

Convex hull

Following examples use the IPython Notebook.

- Methanol gas example
- Hematite drill core example
- Convex hull example
- Pine Creek example 1
- Pine Creek example 2
- Pine Creek example 3
- Support Vector Classification example (new)

## 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)

2. classification

- NormXCorr (class)
- KMeans (class)
- SAM (class)
- SID (class)
- SVC (class) (new)

3. detection

- ACE (function and class)
- CEM (function and class)
- GLRT (function and class)
- MatchedFilter (function and class)
- OSP (function and class)

4. distance

- chebyshev (function)
- NormXCorr (function)
- SAM (function)
- SID (function)

5. eea (endmembers extraction algorithms)

- ATGP (function and class)
- FIPPI (function and class)
- NFINDR (function and class)
- PPI (function and class)

6. formatting

- convert2d (function)
- convert3d (function)
- normalize (function)

7. material_count

- HfcVd (function and class)
- HySime (function and class)

8. noise

- Savitzky Golay filter (class)
- MNF (class)
- Whiten (function and class)

9. sigproc

- bilateral (function)

10. spectro

- convex_hull_removal (function)
- FeaturesConvexHullQuotient (class)
- SpectrumConvexHullQuotient (function with a class interface)
- USGS06SpecLib (class)

11. util

- load_ENVI_file (function) (Python 2.7 only)
- load_ENVI_spec_lib (function) (Python 2.7 only)
- corr (function)
- cov (function)
- display_linear_stretch (function)
- plot_linear_stretch (function)

Glossary

Release notes

Links

## Indices and tables

- Index
- Module Index
- Search Page

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