This is a library of interactive tools and functions for performing linear spectral mixture analysis (LSMA) and spatially adaptive spectral mixture analysis (SASMA). It supports parallel fully constrained least-squares (FCLS) mixture analysis over multiple processes, allowing for very efficient mapping of endmember abundances, both in the spatially adaptive approach and in regular LSMA. In detail, the unmixing module includes tools and functions to support:
- Stacking raster bands from any GDAL file format, including HDF;
- Applying a mask or a combination of masks to a raster;
- Generating raster masks from saturated pixels or using density slicing (e.g., to mask water pixels);
- Applying CFMask output and other quality assurance layers from the USGS;
- Band-wise compositing of reflectance (or fraction/ abundance images);
- Tasseled cap transformation and other indices including NDVI and RNDSI;
- Dimensionality reduction through the Minimum Noise Fraction (MNF);
- Radiometric rectification of a raster time series;
- Visualizing the mixing space of a moderate resolution raster;
- Interactive selection of pixels in the mixing space, which are then captured in a KML file;
- Endmember induction and plotting of endmembers for linear spectral mixture analysis (LSMA);
- Fully constrained least squares (FCLS) unmixing;
- Spatial interpolation of endmembers for spatially adaptive spectral mixture analysis (SASMA);
- Learning of endmember candidates for SASMA using classification and regression trees (CART);
- Validation of unmixing through a forward model of reflectance;
For an overview and tutorial on how to use this library, check out the iPython Notebook(s) in the docs/ folder:
- <https://github.com/arthur-e/unmixing/blob/master/docs/Overview_LSMA_in_Python.ipynb>
- <https://github.com/arthur-e/unmixing/blob/master/docs/Example_Spatially_Adaptive_Spectral_Mixture_Analysis_SASMA.ipynb>
Because this is a scientific library, there are complex dependencies that may be difficult to install. For GNU/Linux systems, particularly Ubuntu, look at install.sh for a guide on installing the system dependencies required for the Python dependencies.
At this time, installation is intended for development purposes only. As such, unmixing should be installed in "editable" mode using pip; see <https://packaging.python.org/distributing/#working-in-development-mode>. From the unmixing directory, where setup.py is found:
$ pip install -e .numpyscipymatplotlibpysptoolsGDALpykmlscikit-learn(For SASMA)
Many of the core packages, particularly NumPy and SciPy, have wide adoption and use. They also take a lot of time and clock cycles to compile into a virtual environment. Consequently, it is recommended that these libraries be installed globally (system-wide).
Other packages should be installed only in the virtual environment. To use both system-wide and local packages within a virtual environment, the virtual environment must be set up with the --system-site-packages option:
sudo apt-get install python3-numpy python3-scipy python3-matplotlib python3-zmq
virtualenv -p /usr/bin/python3.5 --system-site-packages <virtualenv_path>The local packages can be installed within the virtual environment via pip:
source /my/virtualenv/bin/activate
pip install -r REQUIREMENTSSome resources on the dependencies can be found:
matplotlib<http://matplotlib.org/contents.html>- PySptools <http://pysptools.sourceforge.net/index.html>
- PySptools on PyPI <https://pypi.python.org/pypi/pysptools/0.13.0>
Documentation on related (but not required) third-party tools can be found:
- Fmask <https://code.google.com/p/fmask/>
- CFmask <https://code.google.com/p/cfmask/> (A C version of Fmask)
- LEDAPS <https://code.google.com/p/ledaps/>
Many of the tools in this library are designed to work with HSI cubes. While the raster arrays read in from GDAL are p x m x n arrays, HSI cubes are n x m x p arrays, where m is the row index, n is the column index, and p is the spectral band number. It is important to note that the row index n corresponds to the latitude while the column index m corresponds to the longitude. Thus, the coordinates (n, m) can be converted directly to a longitude-latitude pair.