NDLI: An Effective Strategy to Reduce the Labeling Cost in the Definition of Training Sets by Active Learning

Content Provider	IEEE Xplore Digital Library
Author	Demir, B. Minello, L. Bruzzone, L.
Copyright Year	2004
Abstract	This letter proposes a novel strategy for reducing the cost of in situ sample labeling for the definition of training sets by active learning (AL) in the framework of supervised classification of remote sensing images. AL methods define a training set according to an iterative procedure that at each iteration requires the labeling of a set of new samples selected by the classifier. The proposed strategy can be embedded in any AL method to identify the most informative area on the ground where focusing each AL iteration to reduce the overall cost (in terms of time) of labeling. To this end, at each iteration, the most uncertain unlabeled samples are initially identified. Then, the area on the ground (having a size predefined by the user) that has the highest spatial density of informative (i.e., uncertain and diverse) unlabeled samples is selected by the proposed strategy, and the AL technique is applied only to the samples of that area. This results in a decrease of the overall labeling cost with respect to that required by the use of a given technique in a standard way. Experimental results obtained by embedding the presented strategy in different literature active learning methods confirm its effectiveness.
Sponsorship	IEEE Geoscience and Remote Sensing Society
Starting Page	79
Ending Page	83
Page Count	5
File Size	4277271
File Format	PDF
ISSN	1545598X
Volume Number	11
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Labeling Accuracy Remote sensing Standards Uncertainty Kernel training set Active learning (AL) automatic classification clustering in situ data collection remote sensing
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Geotechnical Engineering and Engineering Geology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A cost-sensitive active learning technique for the definition of effective training sets for supervised classifiers

Definition of Effective Training Sets for Supervised Classification of Remote Sensing Images by a Novel Cost-Sensitive Active Learning Method

Remote Sensing Image Classification Exploiting Multiple Kernel Learning

A genetic algorithm based cost-sensitive active learning technique for classification of remote sensing images

A Batch-Mode Active Learning Technique Based on Multiple Uncertainty for SVM Classifier

Interactive Domain Adaptation for the Classification of Remote Sensing Images Using Active Learning

A New Self-Training-Based Unsupervised Satellite Image Classification Technique Using Cluster Ensemble Strategy

Fusion of Extreme Learning Machine and Graph-Based Optimization Methods for Active Classification of Remote Sensing Images

Effective constructing training sets for object detection

An Effective Strategy to Reduce the Labeling Cost in the Definition of Training Sets by Active Learning

Similar Documents

A cost-sensitive active learning technique for the definition of effective training sets for supervised classifiers

Definition of Effective Training Sets for Supervised Classification of Remote Sensing Images by a Novel Cost-Sensitive Active Learning Method

Remote Sensing Image Classification Exploiting Multiple Kernel Learning

A genetic algorithm based cost-sensitive active learning technique for classification of remote sensing images

A Batch-Mode Active Learning Technique Based on Multiple Uncertainty for SVM Classifier

Interactive Domain Adaptation for the Classification of Remote Sensing Images Using Active Learning

A New Self-Training-Based Unsupervised Satellite Image Classification Technique Using Cluster Ensemble Strategy

Fusion of Extreme Learning Machine and Graph-Based Optimization Methods for Active Classification of Remote Sensing Images

Effective constructing training sets for object detection

An Effective Strategy to Reduce the Labeling Cost in the Definition of Training Sets by Active Learning