Cost-sensitive active learning with lookahead: Optimizing field surveys for remote sensing data classification

Claudio Persello, Abdeslam Boularias, Michele Dalponte, Terje Gobakken, Erik Naesset, Bernhard Scholkopf

Research output: Contribution to journalArticleAcademicpeer-review

41 Citations (Scopus)
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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.

Original languageEnglish
Article number6729084
Pages (from-to)6652-6664
Number of pages13
JournalIEEE transactions on geoscience and remote sensing
Issue number10
Publication statusPublished - 2014
Externally publishedYes


  • Active learning (AL)
  • Field surveys
  • Forest inventories
  • Hyperspectral data
  • Image classification
  • Markov decision process (MDP)
  • Support vector machine (SVM)


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