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Feature extraction based on morphological attribute profiles for classification of hyperspectral image
Author(s): Zhen Ye; Yuchan Yan; Lin Bai; Meng Hui
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Paper 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.

Paper Details

Date Published: 9 August 2018
PDF: 7 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060C (9 August 2018); doi: 10.1117/12.2503277
Show Author Affiliations
Zhen Ye, Chang'an Univ. (China)
Yuchan Yan, Chang'an Univ. (China)
Lin Bai, Chang'an Univ. (China)
Meng Hui, Chang'an Univ. (China)

Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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