Unsupervised Hyperspectral Band Selection by Sequential Clustering

Abstract

Hyperspectral data provide detailed information about the spectral properties of an observed scene. Although hyperspectral images contain much information, the reduction of dimensionality of these data is sometimes necessary to minimize their processing complexity. Band selection techniques are ways to perform dimensionality reduction. These techniques consist in choosing some spectral bands that best represent information contained in the original data. In this paper, a hyperspectral band selection approach is proposed. This approach is a sequential clustering-based method. Experiments, based on real hyperspectral data sets, are conducted to assess the performance of the proposed approach and other literature methods. The evaluation is performed by using a supervised pixel classification. The obtained accuracies show that the proposed approach gives satisfactory results and outperforms the tested literature methods.