Dimensionality reduction and scale-space analysis of APEX hyperspectral imagery for tree species discrimination - Université de La Réunion Access content directly
Conference Papers Year : 2018

Dimensionality reduction and scale-space analysis of APEX hyperspectral imagery for tree species discrimination

Abstract

Hyperspectral imagery has high potential for tree species classification. However, high-spectral dimensionality poses computational complexity, such as the Hugh phenomena (a.k.a., the ?curse? of dimensionality). The aim of this study was two-fold: a) to assess spectral dimensionality reduction, for classification of uniform tree stands, in a case of lesser training samples and b) to assess the potential of difference-of-Gaussian for multi-scale representation of tree stands, on dimensionality reduced hyperspectral imagery. A minimum noise transformation was applied on an airborne prism experiment (APEX) image, and 10 MNF-derived components were selected. Multiresolution segmentation and random forest algorithms were used for tree species classification. To assess multi-scale representation, MNF-derived components were convoluted with successive Gaussian filters, and difference-of-Gaussian of each consecutive image were created. A contrast-split-segmentation was used to assess potential tree stands at multi-scales. The classification results using MNF-derived components yielded 93% of overall accuracy on object level, and 0.910 measure of kappa coefficient. A visual inspection of the classification results showed a better performance in a comparison with the classification results of original hyperspectral imagery. Additionally, utilizing DOGs for spatial complexity reduction resulted not only in a spatially simplified image, but also dominant features at different scales were preserved as blobs.
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Dates and versions

hal-01958405 , version 1 (18-12-2018)

Identifiers

  • HAL Id : hal-01958405 , version 1

Cite

Zahra Dabiri, Stefan Lang. Dimensionality reduction and scale-space analysis of APEX hyperspectral imagery for tree species discrimination. GEOBIA 2018 - From pixels to ecosystems and global sustainability ​, Centre d'Etudes Spatiales de la BIOsphère (CESBIO); Office national d'études et de recherches aérospatiales (ONERA); Espace pour le développement (ESPACE DEV); Société T.E.T.I.S, Jun 2018, Montpellier, France. ⟨hal-01958405⟩
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