Paper Info
Title
An Active Learning Heuristic Using Spectral And Spatial Information For Mrf-Based Classification
Abstract
A heuristic utilizing both spectral and spatial information is proposed for active learning. It addresses the issue of iteratively querying most informative training samples with a special focus on spatial-contextual image classification. With the aim to utilize all information during the learning process, the proposed heuristic queries unlabeled pixels considering spectral-spatial inconsistency (SSI), i.e., the unlabeled pixels whose spectral and spatial information indicate different class labels are favored in the active selection. To model spectral-spatial information, a Markov random field (MRF), in which the unary term is defined using the output of a support vector machine and the pairwise term is defined by a multilevel logistic model, is adopted. A new approach to the estimation of the parameters of this MRF model is also incorporated in the proposed method. It aims at taking benefit of spatial information by using the pixels which are representative of the inter-class spatial transitions. A high resolution remotely sensed image is used in the experiments, and the proposed method is proved to be feasible and accurate.
Year
Venue
Keywords
2015
2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
Image classification, active learning, Markov random field, spatial information, potential parameter estimation
Field
DocType
ISSN
Spatial analysis,Pairwise comparison,Computer vision,Heuristic,Pattern recognition,Computer science,Markov random field,Support vector machine,Pixel,Artificial intelligence,Estimation theory,Contextual image classification
Conference
2153-6996
Citations 
PageRank 
References 
0
0.34
11
Authors
4
Name
Order
Citations
PageRank
Bo Hu100.34
Gabriele Moser291976.92
Sebastiano B. Serpico374964.86
Peijun Li4819.08