Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysis

TL;DR

This paper introduces a hierarchical superpixel segmentation method tailored for hyperspectral images, enhancing spectral homogeneity and processing efficiency, and demonstrates its effectiveness in spectral unmixing and classification tasks.

Contribution

The paper presents a novel multiscale hierarchical superpixel segmentation approach that incorporates spectral homogeneity testing, specifically designed for hyperspectral data characteristics.

Findings

Improved spectral homogeneity in superpixels compared to classical SLIC.

Enhanced performance in spectral unmixing and classification tasks.

Competitive results with state-of-the-art hyperspectral segmentation methods.

Abstract

Hyperspectral image (HI) analysis approaches have recently become increasingly complex and sophisticated. Recently, the combination of spectral-spatial information and superpixel techniques have addressed some hyperspectral data issues, such as the higher spatial variability of spectral signatures and dimensionality of the data. However, most existing superpixel approaches do not account for specific HI characteristics resulting from its high spectral dimension. In this work, we propose a multiscale superpixel method that is computationally efficient for processing hyperspectral data. The Simple Linear Iterative Clustering (SLIC) oversegmentation algorithm, on which the technique is based, has been extended hierarchically. Using a novel robust homogeneity testing, the proposed hierarchical approach leads to superpixels of variable sizes but with higher spectral homogeneity when compared to the classical SLIC segmentation. For validation, the proposed homogeneity-based hierarchical method was applied as a preprocessing step in the spectral unmixing and classification tasks carried out using, respectively, the Multiscale sparse Unmixing Algorithm (MUA) and the CNN-Enhanced Graph Convolutional Network (CEGCN) methods. Simulation results with both synthetic and real data show that the technique is competitive with state-of-the-art solutions.