論文

2019年7月

Hyperspectral Image Classification Using Tensor CP Decomposition

International Geoscience and Remote Sensing Symposium (IGARSS)
  • Mohamad Jouni
  • ,
  • Mauro Dalla Mura
  • ,
  • Pierre Comon

開始ページ
1164
終了ページ
1167
記述言語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/IGARSS.2019.8898346

© 2019 IEEE. 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.

リンク情報
DOI
https://doi.org/10.1109/IGARSS.2019.8898346
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85077697501&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85077697501&origin=inward

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