Hierarchical homogeneity-based superpixel segmentation: application to hyperspectral image analysis

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 a...

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Bibliographic Details
Published in:International journal of remote sensing 2024-09, Vol.45 (17), p.6004-6042
Main Authors: Ayres, L. C., de Almeida, S. J. M., Bermudez, J. C. M., Borsoi, R. A.
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
classification
Clustering
Computer Science
Computer Vision and Pattern Recognition
hierarchical
Hierarchies
Homogeneity
Hyperspectral data
Hyperspectral imaging
Image analysis
Image segmentation
Spatial data
Spatial variability
Spatial variations
Spectral signatures
superpixels
unmixing
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Summary: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.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2024.2384098