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Task-driven nonlocal uncertainty analysis for hyperspectral image classification using a context-based Bayesian framework
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AI
DOI：10.1080/01431161.2021.1954260.png)
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摘要
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Spatial contextual information could help to further improve hyperspectral image classification performance. However, most spatial-spectral hyperspectral image classification methods introduce spatial information primarily by combining pixels in a small neighbourhood. These strategies consider only limited spatial structures during classification, which reduces classification accuracy. To this end, a novel, context-based Bayesian framework with a task-driven nonlocal uncertainty analysis model is proposed to combine spectral-spatial features for hyperspectral image classification. Task-dependent measures are often needed to formulate similarity, which is used to express various relationships. Based on this observation, in this paper, we introduce a task-driven similarity measure based on nonlocal uncertainty analysis for hyperspectral classification. Nonlocal-local spatial a priori is constructed using the Dempster-Shafer theory after analysing the spatial uncertainty to a nonlocal extent, which further expands the sample capacity and spatial extent. Furthermore, an improved iterated conditional mode is proposed to acquire the energy function's local minimum in the context-based Bayesian framework. The experimental results, using both simulated data and two popular hyperspectral data sets, indicate that the proposed methods can significantly improve classification accuracy when compared with other state-of-the-art methods.
Keyword:
SEGMENTATION
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MODELS
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IF:
2.6
论文数:
1.2W
被引数:
2.7W
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