Robust input layer for neural networks for hyperspectral classification of data with missing bands

Hyperspectral classification using artificial neural networks is commonly applied on camera dependent interpolated data, or on the results of a dimensionality reduction algorithm. While these methods usually produce satisfactory results, they have severe limitations when part of the spectrum is missing, for example when parts of the image are overexposed or affected by bad pixels. This article presents an input layer based on the Haar transform for artificial neural networks used for hyperspectral data classification. This input layer is designed to perform efficiently with incomplete data and is independent of the specific bands used by the camera. This could enable providing pre-trained neural networks, which can be used with a camera with different specifications than the one used for training. This paper shows that a classifier for mineral identification built using this approach performs better than standard normalization on incomplete spectra, and similarly on complete spectra. Additionally, it shows that such a classifier matches local spectral features, and therefore that the artificial neural network is matching the spectrum shape.