Overview of Hierarchical Models for Hyperspectral Image Classification

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 metadatas

Cited literature [13 references]  Display  Hide  Download

https://hal.inria.fr/hal-00752928
Contributor : Yuliya Tarabalka <>
Submitted on : Friday, November 16, 2012 - 4:30:45 PM
Last modification on : Saturday, January 27, 2018 - 1:31:40 AM
Long-term archiving on : Saturday, December 17, 2016 - 11:30:28 AM

File

2012UkrObrazTarabalka.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00752928, version 1

Collections

Citation

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⟩

Share

Metrics

Record views

297

Files downloads

220