Material Based Boundary Detection in Hyperspectral Images

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Author(s)
Al-Khafaji, Suhad Lateef
Zia, Ali
Zhou, Jun
Liew, Alan Wee-Chung
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Guo, Y
Li, H
Cai, W
Murshed, M
Wang, Z
Gao, J
Feng, DD
Date
2017
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Abstract

Boundary detection in hyperspectral image (HSI) is a challenging task due to high data dimensionality and the that is distributed over the spectral bands. For this reason, there is a dearth of research on boundary detection in HSI. In this paper, we propose a spectral-spatial feature based statistical co-occurrence method for this task. We adopt probability density function (PDF) to estimate the co-occurrence of features at neighboring pixel pairs. Such cooccurrence is rare at the boundary and repeated within a region. To fully explore the material information embedded in HSI, joint spectral-spatial features are extracted at each pixel. The PDF values are then used to construct an affinity matrix for all pixels. After that, a spectral clustering algorithm is applied on the affinity matrix to produce boundaries. Our algorithm is evaluated on a dataset of real-world HSIs and compared with two alternative approaches. The results show that the proposed method is very effective in exploring object boundaries from HSI images.

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2017 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING - TECHNIQUES AND APPLICATIONS (DICTA)
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Artificial intelligence
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