Spectral library pruning based on classification techniques

Abstract

Spectral unmixing is an active research area in remote sensing. The direct use of the spectral libraries in spectral unmixing is increased by increasing the availability of the libraries. In this way, the spectral unmixing problem is converted into a sparse regression problem that is time-consuming. This is due to the existence of irrelevant spectra in the library. So these spectra should be removed in some way. In this paper, a machine learning approach for spectral library pruning is introduced. At first, the spectral library is clustered based on a simple and efficient new feature space. Then the training data needed to learn a classifier are extracted by adding different noise levels to the clustered spectra. The label of the training data is determined based on the results of spectral library clustering. After learning the classifier, each pixel of the image is classified using it. For pruning the library, the spectra with the labels that none of the image pixels belong to, are removed. We use three classifiers, decision tree, neural networks and k-nearest neighbor to determine the effect of applying different classifiers. The results compared here show that the proposed method works well in noisy images.