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WAVANGLET: an efficient supervised classifier for hyperspectral images

Abstract : The new generation of imaging spectrometers on board 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: A. Selection of a library composed of reference spectra; B. Application of a Daubechies wavelet transform to referenced spectra and determination of the wavelet subspace that best separates all referenced spectra; C. 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: H_{2}O ice, CO_{2} ice and dust. The ``wavanglet'' method is implemented to detect these major compounds on near-infrared hyperspectral images acquired by the OMEGA instrument on board the Mars Express spacecraft. With an overall accuracy of 89%, "wavanglet'' outperforms two generic methods: Band Ratio (57% accuracy) and Spectral Feature Fitting^{\mathrm{TM}} (83% accuracy). The quantitative detection limits of "wavanglet'' are also evaluated in terms of abundance for H_{2}O and CO_{2} ices in order to improve the interpretation of the masks.
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Frédéric Schmidt, Sylvain Douté, Bernard Schmitt. WAVANGLET: an efficient supervised classifier for hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2007, 45 (5), pp.1374--1385. ⟨10.1109/TGRS.2006.890577⟩. ⟨hal-00325458⟩

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