Hyperspectral Image Classification Based on Mathematical Morphology and Tensor Decomposition

Hyperspectral Image (HSI) classification refers to classifying hyperspectral data into features, where labels are given to pixels sharing the same features, distinguishing the present materials of the scene from one another. Naturally a HSI acquires spectral features of pixels, but spatial features...

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Vydáno v:Mathematical Morphology - Theory and Applications Ročník 4; číslo 1; s. 1 - 30
Hlavní autoři: Jouni, Mohamad, Mura, Mauro Dalla, Comon, Pierre
Médium: Journal Article
Jazyk:angličtina
Vydáno: De Gruyter Open 01.01.2020
De Gruyter
Témata:
15A69
94A08
Attribute Profiles
Computer Science
Hyperspectral Imagery
Image Processing
Mathematical Morphology
Remote Sensing Image
Scene Classification
Tensor Decomposition
Scene Classification MSC: 15A69
Tensor Decomposition
94A08
Scene Classification
Attribute Profiles
Ten- sor Decomposition
Remote Sensing Image
Hyperspectral Imagery
Mathematical Morphology
ISSN:2353-3390, 2353-3390
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Popis
Shrnutí:Hyperspectral Image (HSI) classification refers to classifying hyperspectral data into features, where labels are given to pixels sharing the same features, distinguishing the present materials of the scene from one another. Naturally a HSI acquires spectral features of pixels, but spatial features based on neighborhood information are also important, which results in the problem of spectral-spatial classification. There are various ways to account to spatial information, one of which is through Mathematical Morphology, which is explored in this work. A HSI is a third-order data block, and building new spatial diversities may increase this order. In many cases, since pixel-wise classification requires a matrix of pixels and features, HSI data are reshaped as matrices which causes high dimensionality and ignores the multi-modal structure of the features. This work deals with HSI classification by modeling the data as tensors of high order. More precisely, multi-modal hyperspectral data is built and dealt with using tensor Canonical Polyadic (CP) decomposition. Experiments on real HSI show the effectiveness of the CP decomposition as a candidate for classification thanks to its properties of representing the pixel data in a matrix compact form with a low dimensional feature space while maintaining the multi-modality of the data.
ISSN:2353-3390
2353-3390
DOI:10.1515/mathm-2020-0001