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Recent developments in hyperspectral sensors have made it possible to acquire hyperspectral images (HSI) with higher spectral and spatial resolution. Hence, it is now possible to extract detailed information about relatively smaller structures. Despite these advantages, HSI suffers from many challenges also, like higher spatial variability of spectral signatures, the Hughes effect due to higher dimensionality, and a limited number of labeled training samples compared to the dimensions of the spectral space. Superpixels can be a potentially effective tool in tackling these challenges. Superpixel segmentation is a process of segmenting the spatial image into several semantic subregions with similar characteristic features. Such grouping by similarity can significantly ease the subsequent processing steps. Because of this, superpixels have been successfully applied to various fields of HSI processing such as classification, spectral unmixing, dimensionality reduction, band selection, active learning (AL), denoising, and anomaly detection. This article focuses on classification, presenting a detailed survey of superpixel segmentation approaches for the classification of HSI. The superpixel creation algorithm framework and postprocessing frameworks for superpixels in HSI are also analyzed. Also, a brief description of various application areas of superpixels is provided. An experimental analysis of existing superpixel segmentation approaches is also provided in this article, supported by quantitative results on standard benchmark datasets. The challenges and future research directions for the implementation of superpixel algorithms are also discussed.

A Survey on Superpixel Segmentation as a Preprocessing Step in Hyperspectral Image Analysis