WAVANGLET: An Efficient Supervised Classifier for Hyperspectral Images

The new generation of imaging spectrometers onboard planetary missions usually produce hundreds to thousands of images a year, each made up of a thousand to a million spectra with typically several hundred wavelengths. Such huge datasets must be analyzed by efficient yet accurate algorithms. A supervised automatic classification method (hereafter called "wavanglet") is proposed to identify spectral features and classify images in spectrally homogeneous units. It uses four steps: (1) selection of a library composed of reference spectra; (2) application of a Daubechies wavelet transform to referenced spectra and determination of the wavelet subspace that best separates all referenced spectra; and (3) in this selected subspace, determination of the best threshold on the spectral angle to produce detection masks. This application is focused on the Martian polar regions that present three main types of terrains: H2O ice, CO2 ice, and dust. The wavanglet method is implemented to detect these major compounds on near-infrared hyperspectral images acquired by the OMEGA instrument onboard the Mars Express spacecraft. With an overall accuracy of 89%, wavanglet outperforms two generic methods: band ratio (57% accuracy) and spectral feature fitting (83% accuracy). The quantitative detection limits of wavanglet are also evaluated in terms of abundance for H2O and CO2 ices in order to improve the interpretation of the masks