NDLI: Multiple instance and context dependent learning in hyperspectral data

Content Provider	IEEE Xplore Digital Library
Author	Torrione, P. Ratto, C. Collins, L.M.
Copyright Year	2009
Description	Author affiliation: Duke University, Department of Electrical and Computer Engineering, Box 90291, Durham NC, 27708, USA (Torrione, P.; Ratto, C.; Collins, L.M.)
Abstract	Hyperspectral imaging (HSI) is a powerful tool for various remote sensing tasks including agricultural modeling and landmine/ 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 hyperspectral 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.
Starting Page	1
Ending Page	4
File Size	409093
Page Count	4
File Format	PDF
ISBN	9781424446865
DOI	10.1109/WHISPERS.2009.5289021
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-26
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral sensors Landmine detection Multiple instance Hyperspectal Context dependent Statistics Remote sensing Landmines Supervised learning Training data Labeling Hyperspectral imaging Context modeling Logistics
Content Type	Text
Resource Type	Article

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