{'fraction_non_alphanumeric': 0.01632208922742111, 'fraction_numerical': 0.0, 'mean_word_length': 4.935483870967742, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Unsupervised endmember extraction: Application to hyperspectral images from Mars

In this paper, we try to identify and quantify the chemical species present on the surface of planet Mars with the help of hyperspectral images provided by the instrument OMEGA. For this purpose, we suppose that the spectrum of each pixel is a linear mixture of the spectra of different endmembers. From this linear mixture hypothesis, our work is divided into two steps. Firstly, we propose a new unsupervised method for estimating the number of endmembers based on the eigenvalues of covariance and correlation matrix of the hyperspectral data. This method is then validated on synthetic data. With the help of the number estimated by the precedent step, we use the vertex component analysis (VCA) to extract the spectra and the abundances of the end members. The results on hyperspectral image taken by the instrument OMEGA are shown.