GatorSense Sparsity Promoting Iterated Constrained Endmembers Toolkit - Python Implementation

## Project description

# SPICE

Sparsity Promoting Iterated Constrained Endmembers

NOTE: If the SPICE Algorithm is used in any publication or presentation, the following reference must be cited:

Zare, A.; Gader, P.; , "Sparsity Promoting Iterated Constrained Endmember Detection in Hyperspectral Imagery,"" IEEE Geoscience and Remote Sensing Letters, vol.4, no.3, pp.446-450, July 2007.

NOTE: If the code is used anywhere or in any presentation or publication, include the following reference: Caleb Robey, Taylor Glenn, Alina Zare, & Paul Gader. (2018, October 24). GatorSense/SPICE_py v1.0 (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.1470878

The SPICE Algorithm in Python is run using the function:

from SPICE import * endmembers, P = SPICE(inputData, parameters)

If you would like to use the default parameters (described below), use the command:

`parameters = SPICEParameters()`

The inputData input is a DxM matrix of M input data points with D dimensions. Each of the M pixels has D spectral bands. Each pixel is a column vector.

This form can be achieved from a three-dimensional hyperspectral numpy array using the following commands:

import numpy as np inputData = np.reshape(inputData, (inputData.shape[0]*inputData.shape[1], inputData.shape[2]))

The parameters input is a struct with the following fields:

```
parameters.u : This is the regularization parameter that trades off between the RSS and SPT terms.
parameters.gamma : Gamma constant for the SPT term, controls the degree of sparsity desired
parameters.changeThresh : Stopping Criteria, Set this to the desired change threshold for the objective function
parameters.M : Number of Initial Endmembers
parameters.iterationCap : Maximum Number of Iterations
parameters.endmemberPruneThreshold : This is the pruning threshold for endmembers
parameters.produceDisplay : Set this to 1 if progress display is desired, 0 otherwise
parameters.initEM = None : By setting this to None, the algorithm randomly selects initial endmembers from the input data. You can also provide initial endmembers by inputting a matrix of endmembers. Every column is one endmember. The number of endmembers should match parameters.M.
parameters.qp_solver = 'cvxopt' : This can be 'cvxopt' or 'QPP'. cvxopt is slower, but may be used on matrices where QPP hits errors
parameters.prescale = True : Set this to True to normalize input data between 0 and 1
```

The parameters structure can be generated using the SPICEParameters.m function.

unmix2, which is imported with `from SPICE import *`

, is a required helper function which unmixes the data points given the endmembers.

**Note: Often the parameters must be adjusted for a particular data set. Generally, u is set to between 0.001 and 0.1 depending on noise levels in the data. gamma is generally set to a value between 1 and 10 depending on the data set. We have also found that SPICE has improved performance if the data has been normalized between 0 and 1 before running SPICE (e.g. Subtracting the minimum and then dividing by the max OR normalizing each spectrum by its L2 norm).**

### Running the Demo

This repository includes sample data in the form of a pickle file called "hsi_data.pkl". This contains a hyperspectral data cube, which can be analyzed by the SPICE algorithm.

To run the algorithm, use the command:

`python spice_py_demo.py`

The algorithm should run for no more than 40 iterations (typically much less) and will detect 4 or 5 endmembers, depending on the randomized endmember initialization parameter. After the algorithm is finished, you will be prompted to choose whether you would like to graph the output. Choose yes (Y) and a figure will appear with the proportions of each endmember in the context of the original image. Expand this window for a cleaner view of the plots. After closing this figure window, a plot of the wavelength and reflectance of each endmember will appear.

### Requirements

It is recommended that you use a python virtual environment for this project using the following commands from the SPICE_py directory:

`pip install virtualenv`

(If you don't have the package installed already)`python3 -m venv spice_env`

(Linux/Mac OSX)`source ./spice_env/bin/activate`

(Linux/Mac OSX)`python -m venv spice_env`

(Windows)`spice_env\Scripts\activate.bat`

(Windows)

This program uses the python packages in the requirements.txt file. Those can be installed using the command:

`pip install -r requirements.txt`

This must also be done from the SPICE_py directory. If you run into issues, particularly on Windows, you may want to consider using conda forge and conda environments for the install.

Note: This code uses qpsolvers (see the QPP.py file) by Stephane Caron stephane.caron@normalesup.org

### Questions

If you have any questions, please contact:

Alina Zare

Electrical and Computer Engineering

University of Florida

azare [at] ufl.edu

This product is Copyright (c) 2018 All rights reserved.

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