NDLI: Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning

Content Provider	IEEE Xplore Digital Library
Author	Jun Li Bioucas-Dias, J.M. Plaza, A.
Copyright Year	1980
Abstract	This paper introduces a new supervised Bayesian approach to hyperspectral image segmentation with active learning, which consists of two main steps. First, we use a multinomial logistic regression (MLR) model to learn the class posterior probability distributions. This is done by using a recently introduced logistic regression via splitting and augmented Lagrangian algorithm. Second, we use the information acquired in the previous step to segment the hyperspectral image using a multilevel logistic prior that encodes the spatial information. In order to reduce the cost of acquiring large training sets, active learning is performed based on the MLR posterior probabilities. Another contribution of this paper is the introduction of a new active sampling approach, called modified breaking ties, which is able to provide an unbiased sampling. Furthermore, we have implemented our proposed method in an efficient way. For instance, in order to obtain the time-consuming maximum a posteriori segmentation, we use the α-expansion min-cut-based integer optimization algorithm. The state-of-the-art performance of the proposed approach is illustrated using both simulated and real hyperspectral data sets in a number of experimental comparisons with recently introduced hyperspectral image analysis methods.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	3947
Ending Page	3960
Page Count	14
File Size	1138568
File Format	PDF
ISSN	01962892
Volume Number	49
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Image segmentation Training Kernel Pixel Approximation algorithms sparse multinomial logistic regression (MLR) Active learning graph cuts hyperspectral image segmentation ill-posed problems integer optimization mutual information (MI)
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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