Skip to Main content Skip to Navigation
New interface
Conference papers

Overview of Hierarchical Models for Hyperspectral Image Classification

Yuliya Tarabalka 1 
Abstract : Hyperspectral imaging enables accurate classification, but also presents challenges of high-dimensional data analysis. While pixelwise classification methods classify each pixel independently, recent studies have shown the advantage of considering the correlations between spatially adjacent pixels for accurate image analysis. This paper provides an overview of the available hierarchical models for spectral-spatial classification of hyperspectral images. The two most recent models are experimentally compared on a 102-band ROSIS image of the Center of Pavia, Italy. The experimental results demonstrate that classification methods using hierarchical models are attractive for remote sensing image analysis.
Document type :
Conference papers
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download
Contributor : Yuliya Tarabalka Connect in order to contact the contributor
Submitted on : Friday, November 16, 2012 - 4:30:45 PM
Last modification on : Thursday, January 20, 2022 - 4:15:32 PM
Long-term archiving on: : Saturday, December 17, 2016 - 11:30:28 AM


Files produced by the author(s)


  • HAL Id : hal-00752928, version 1



Yuliya Tarabalka. Overview of Hierarchical Models for Hyperspectral Image Classification. UkrOBRAZ - Signal/Image Processing and Pattern Recognition Conference, Oct 2012, Kyiv, Ukraine. pp.1-4. ⟨hal-00752928⟩



Record views


Files downloads