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Cost-sensitive active learning with lookahead: optimizing field surveys for remote sensing data classification
- Publication Year :
- 2014
- Publisher :
- IEEE / Institute of Electrical and Electronics Engineers Incorporated, 2014.
-
Abstract
- Active learning typically aims at minimizing the number of labeled samples to be included in the training set to reach a certain level of classification accuracy. Standard methods do not usually take into account the real annotation procedures and implicitly assume that all samples require the same effort to be labeled. Here, we consider the case where the cost associated with the annotation of a given sample depends on the previously labeled samples. In general, this is the case when annotating a queried sample is an action that changes the state of a dynamic system, and the cost is a function of the state of the system. In order to minimize the total annotation cost, the active sample selection problem is addressed in the framework of a Markov decision process, which allows one to plan the next labeling action on the basis of an expected long-term cumulative reward. This framework allows us to address the problem of optimizing the collection of labeled samples by field surveys for the classification of remote sensing data. The proposed method is applied to the ground sample collection for tree species classification using airborne hyperspectral images. Experiments carried out in the context of a real case study on forest inventory show the effectiveness of the proposed method.
- Subjects :
- Computer science
Active learning (machine learning)
Data classification
Active Learning
Context (language use)
Sample (statistics)
Hyperspectral data
computer.software_genre
Machine learning
Forest inventories
Field Surveys
Electrical and Electronic Engineering
Support Vector Machine (SVM)
Remote sensing
Training set
Contextual image classification
Image Classification
business.industry
Support vector machine
Markov decision process (MDP)
Active learning
General Earth and Planetary Sciences
Settore ING-INF/03 - TELECOMUNICAZIONI
Markov decision process
Data mining
Artificial intelligence
business
computer
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....0b997ccc67dffa5f73bca99585d4e12d