Hyperspectral Image Classification Using Tensor CP Decomposition

Mohamad Jouni 1 Mauro Dalla Mura 1 Pierre Comon 2
1 GIPSA-SIGMAPHY - SIGMAPHY
GIPSA-DIS - Département Images et Signal
2 GIPSA-CICS - CICS
GIPSA-DIS - Département Images et Signal
Abstract : Image classification has been at the core of remote sensing applications. Optical remote sensing imaging systems naturally acquire images with spectral features corresponding to pixels. Spectral classification ignores the spatial distribution of the data which is becoming more relevant with the development of spatial resolution sensors, and many works aim to incorporate spatial features based on neighborhood through for example, Mathematical Morphology (MM). Additionally, one could stack multiple morphological transformations of the image resulting in a highly complex block of data. Since classification is a tool that requires a matrix of samples and features, and simply stacking the different sets of features can lead to the problem of high dimensionality, we propose a way to create a matrix of low dimensional feature space by modeling the data as tensors and thanks to Canonical Polyadic (CP) decomposition. Experiments on real image show the effectiveness of the proposed method.
Document type :
Preprints, Working Papers, ...
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01998220
Contributor : Mohamad Jouni <>
Submitted on : Thursday, February 14, 2019 - 12:22:26 PM
Last modification on : Thursday, March 7, 2019 - 11:38:47 AM

File

Paper_IGARSS_2019_V1.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01998220, version 1

Citation

Mohamad Jouni, Mauro Dalla Mura, Pierre Comon. Hyperspectral Image Classification Using Tensor CP Decomposition. 2019. ⟨hal-01998220⟩

Share

Metrics

Record views

46

Files downloads

55