Multiple instance and context dependent learning in hyperspectral data

Published

Conference Paper

Hyperspectral imaging (HSI) is a powerful tool for various remote sensing tasks including agricultural modeling and land-mine/unexploded ordnance clearance. Although the application of standard supervised learning techniques to HSI data has previously been explored, several aspects of hyperspectral data collection and ground truth labeling make some of the assumptions underlying standard machine learning techniques invalid. For example, HSI is highly dependent upon local environmental conditions, and pixel-by-pixel labels for HSI data are often not available. As a result, data from hyper-spectral sensing under various scenarios is not typically i.i.d., and correct data labels must be inferred from training data while learning decision boundaries. In this work we explore two possible solutions to these problems: context-dependent learning for overcoming variations between collections, and multiple instance learning for simultaneously inferring local target labels and global target decision boundaries. Results are compared to standard logistic discriminant classification approaches. © 2009 IEEE.

Full Text

Duke Authors

Cited Authors

  • Torrione, P; Ratto, C; Collins, LM

Published Date

  • December 21, 2009

Published In

  • Whispers '09 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

International Standard Book Number 13 (ISBN-13)

  • 9781424446872

Digital Object Identifier (DOI)

  • 10.1109/WHISPERS.2009.5289021

Citation Source

  • Scopus