Classification of hyperspectral images by tensor modeling and additive morphological decomposition

Pixel-wise classification in high-dimensional multivariate images is investigated. The proposed method deals with the joint use of spectral and spatial information provided in hyperspectral images. Additive morphological decomposition (AMD) based on morphological operators is proposed. AMD defines a...

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Veröffentlicht in:Pattern recognition Jg. 46; H. 2; S. 566 - 577
Hauptverfasser: Velasco-Forero, Santiago, Angulo, Jesus
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.02.2013
Elsevier
Schlagworte:
Additives
Algorithms
Applied sciences
Artificial intelligence
Classification
Computer Science
Computer science; control theory; systems
Decomposition
Engineering Sciences
Exact sciences and technology
Hyperspectral images
Information, signal and communications theory
Mathematical analysis
Mathematical morphology
Pattern recognition
Pattern recognition. Digital image processing. Computational geometry
Pixelwise classification
Signal and Image processing
Signal processing
Spectra
Telecommunications and information theory
Tensor modeling
Tensors
Hyperspectral images
Pixelwise classification
Mathematical morphology
Tensor modeling
Performance evaluation
Pixelwise
Pattern recognition
Algorithm
Modeling
Statistical method
Hyperspectral imaging sensor
Dimension reduction
Multiscale method
Tensor calculus
Principal component analysis
Hyperspectral
Morphology
ISSN:0031-3203, 1873-5142
Online-Zugang:Volltext
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Zusammenfassung:Pixel-wise classification in high-dimensional multivariate images is investigated. The proposed method deals with the joint use of spectral and spatial information provided in hyperspectral images. Additive morphological decomposition (AMD) based on morphological operators is proposed. AMD defines a scale-space decomposition for multivariate images without any loss of information. AMD is modeled as a tensor structure and tensor principal components analysis is compared as dimensional reduction algorithm versus classic approach. Experimental comparison shows that the proposed algorithm can provide better performance for the pixel classification of hyperspectral image than many other well-known techniques. ► Additive morphological decomposition without any loss of information is proposed. ► Decomposition is modeled as a tensor structure. ► Tensor PCA is compared versus PCA. ► Proposed workflow performs better than other techniques in HSI classification.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2012.08.011