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

• URL: http://arxiv.org/abs/2407.15321v1
• Date: Mon, 22 Jul 2024 01:20:32 GMT
• Title: Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysis
• Authors: Luciano Carvalho Ayres, Sérgio José Melo de Almeida, José Carlos Moreira Bermudez, Ricardo Augusto Borsoi,
• Abstract summary: We propose a multiscale superpixel method that is computationally efficient for processing hyperspectral data. The proposed hierarchical approach leads to superpixels of variable sizes but with higher spectral homogeneity. For validation, the proposed homogeneity-based hierarchical method was applied as a preprocessing step in the spectral unmixing and classification tasks.
• Score: 11.612069983959985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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