A Multiscale Superpixel-Level Group Clustering Framework for Hyperspectral Band Selection

Abstract

Hyperspectral imagery (HSI) contains hundreds of bands, which provide a wealth of spectral information and enable better characterization of features. However, the excessive dimensionality also poses a dimensional disaster for subsequent processing. Fortunately, band selection (BS) gives a straightforward and effective way to pick out a subset of bands with rich information and low correlation. Although many hyperspectral BS methods, especially clustering-based ones, have been proposed by researchers in recent years, the contextual information of adjacent bands and the spatial structural information of materials are not well investigated. Therefore, in this article, a multiscale superpixel-level group-clustering framework (MSGCF) has been proposed for hyperspectral BS. Different from previous, a new superpixel-level distance measure is elaborately utilized to group and cluster the spectral bands, which jointly considers the spectral context and spatial structure information. Concretely, to preserve the spatial structural information of HSI, multiple superpixel segmentation is first performed to generate superpixel maps in multiscales, which enables complementarity of multiple superpixel segmentation algorithms and adaptation to diverse scales of land cover types. Second, the grouping and clustering paradigm is introduced to conduct the contextual information among bands. Here the maximum points of superpixel-level KL- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> distance of adjacent bands are adopted as partition points to separate bands into groups, which encourages adjacent bands with strong correlation to be divided into the same group. Third, a superpixel-level fast density-based clustering method (SuFDPC) with superpixel-level <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2, 1}$ </tex-math></inline-formula> distance is developed to select representative bands in every group. Finally, BS results are achieved with a ranking-based voting strategy by concerning information entropy and frequency of occurrence in a unified scheme. A series of ablation analyses and experimental comparisons on four real HSI datasets have been conducted, as well as similarity comparisons for the selected bands. The experimental results consistently demonstrated the effectiveness of our MSGCF approach. The codes of this work will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://jiasen.tech/papers/</uri> for the sake of reproducibility.