Feature extraction based on morphological attribute profiles for classification of hyperspectral image

Zhen Ye; Yuchan Yan; Lin Bai; Meng Hui

doi:10.1117/12.2503277

9 August 2018 Feature extraction based on morphological attribute profiles for classification of hyperspectral image

Zhen Ye, Yuchan Yan, Lin Bai, Meng Hui

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108060C (2018) https://doi.org/10.1117/12.2503277
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

Traditional hyperspectral image classification typically uses raw spectral signatures without considering the spatial characteristics. In this paper, we proposed a novel method for hyperspectral image classification based on morphological attribute profiles. We employed independent component analysis for dimensionality reduction and designed an extended multiple attribute profiles (EMAP) to extract spatial features in ICA-induced subspaces. For accurate classification, we proposed a Bayesian maximum a posteriori formulation that couples EMAPs-based feature extraction for the class-conditional probability with an MRF-based prior. Experimental results show that the proposed method substantially outperforms traditional and state-of-the-art methods tending to result in smoother classification maps with fewer erroneous outliers.

Citation Download Citation

Zhen Ye, Yuchan Yan, Lin Bai, and Meng Hui "Feature extraction based on morphological attribute profiles for classification of hyperspectral image", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060C (9 August 2018); https://doi.org/10.1117/12.2503277

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KEYWORDS

Feature extraction

Hyperspectral imaging

Image classification

Magnetorheological finishing

Electroactive polymers

Independent component analysis

Classification systems

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