Paper Info
Title
Active Learning for Domain Adaptation in Classification of Remote Sensing Data by Minimizing Expected Error with Diversity Maximization
Abstract
This paper investigates a method addressing remote sensing image classification based on the domain adaptation (DA) and active learning (AL). The key idea of our method is to retrain the classifier by using the available labeled samples from source domain, and adding minimum number of the most informative samples with active queries in the target domain. The active learning approaches provide pool of candidate samples based on different query functions with the assessment of uncertainty and diversity. The uncertainty criterion evaluates the algorithm’ confidence in order to correctly classify the considered samples, while the diversity criterion decreasing the redundancy between the selected samples by choosing a set of unlabeled samples with more diversity. So, based on this model, we exploit both two criteria in order to choose the most informative samples are selected iteratively at the active learning procedure. In this work, we have proposed a novel uncertainty criteria based minimum expected error (MEE), and also use angular based diversity (ABD) as diversity criteria. The experimental outcomes prove the superiority of the proposed approach to counterpart methods.
Year
DOI
Venue
2018
10.1109/ISTEL.2018.8660974
2018 9th International Symposium on Telecommunications (IST)
Keywords
Field
DocType
Remote Sensing,Domain Adaptation,Active Learning,Classification,Uncertainty,Diversity
Active learning,Domain adaptation,Computer science,Remote sensing,Exploit,Redundancy (engineering),Classifier (linguistics),Contextual image classification,Maximization
Conference
ISBN
Citations 
PageRank 
978-1-5386-8274-6
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Arash Saboori111.36
Hassan Ghassemian239634.04