Welcome to PlanetGLLiM documentation#
What is PlanetGLLiM ?#
PlanetGLLiM is a software for the physical analysis of multi-dimensional data in planetary remote sensing. It is specifically tailored to handle inversion of reflectance models given sets of multi-angular measurements of light reflected by natural granular media.
The application is built around a computationally efficient kernel that implements in C++ a machine learning statistical technique called GLLiM. The kernel, named Kernelo, can be used as a library or a Python module to solve inverse problems in other areas of application.
Both PlanetGLLiM and Kernelo are open source and freely available under the CeCILL license.
Features: What can I do with PlanetGLLiM ?#
Choose among a large variety of reflectance models to perform the analysis.
PlanetGLLiM natively implements in its kernel the two most popular reflectance models used in planetary science: the Hapke and the Shkuratov reflectance models. It also offers the possibility to build custom models in the form of a single Python class.
Analyse two kinds of spectro-(gonio)-photometric data.
Reflectance spectra acquired by spectro-photo-goniometers in the laboratory according to different illumination and viewing geometries.
Remote sensing products generated by multispectral sensors during multi-angular satellite acquisitions. The great number of reflectance measurements \(N_{obs}\), are organized in data cubes resulting from the combination of spectral and spatial sampling of the scene.
Drive the application with an easy to use graphical user interface.
The PlanetGLLiM graphical interface allows the user to configure, save, load, and run an experiment. In addition it provides a quick look on the results that can be saved in their complete form locally in a zip archive. During the experiment execution a follow-up through the different steps is shown while a detailed log is also displayed.
Who is developing PlanetGLLiM ?#
PlanetGLLiM Repository:
PlanetGLLiM Contributors:
Sylvain Douté <sylvain.doute@univ-grenoble-alpes.fr> IPAG, CNRS, scientific manager.
Florence Forbes <florence.forbes@inria.fr> INRIA, co-investigator.
Luc Meyer <luc.meyer@inria.fr>, INRIA, software developer.
Samuel Heidmann <samuel.heidmann@inria.fr>, INRIA, software developer.
Stan Borkowski <stan.borkowski@inria.fr>, INRIA, project manager.
Sami Djouadi <fs_djouadi@esi.dz>, former intern, initial implementation.
Benoit Kugler <benoit.kugler@inria.fr>, former PhD student and external initial contributor.