Hyperspectral superpixel extraction using boundary updates based on optimal spectral similarity metric

The high spectral resolution of hyperspectral images (HSI) requires a heavy processing load. Assigning each pixel to a group in the image, which is called superpixel, and processing the superpixels instead of the pixels is resorted as a means to overcome this challenge in the hyperspectral literature. In this paper, we propose an algorithm to segment a hyperspectral image into superpixels by means of iteratively updating the boundary pixels of superpixels. We first explore the optimal similarity metric for the boundary pixel updates with the contraint of keeping the superpixel boundaries aligned with the object boundaries in the image. We investigate two approaches for similarity detection between pixels during this update, first comparing the hyperspectral pixels individually, and second, comparing the pixels by using also their neigborhood. The spectral similarity metrics used for investigation are selected as spectral angle mapping (SAM) [1], spectral information divergence (SID) [2] and spatial coherence distance [3] due to their common usage. The proposed approach is compared with a pioneer state-of-the-art superpixel algorithm, SLIC [4], and its superiority is verified in terms of the superpixelization performance metrics, namely boundary recall and undersegmentation error [5].