Adaptive Self-Learned Active Learning Framework for Hyperspectral Classification

Nasehe Jamshidpour, Enayat Hosseini Aria, Abdolreza Safari, Saeid Homayouni

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

This paper proposes a novel self-learned integrated framework of active learning (AL) and semi-supervised learning (SSL). SSL methods try to estimate a certain semilabel for unlabeled samples. While AL methods select the most informative unlabeled samples for the current classifying model and provide their labels by human expertise. An excessive human-machine interaction is required for labeling the selected instances. Whereas, providing reliable labels is a sensitive, time-consuming and expensive step. In our framework, we try to decrease the required human supervision by incorporating SSL method. In addition, the participation rate of each AL and SSL methods in the framework is adaptive and determined based on the certainty of the classifier at each iteration. The experiments were carried out on Pavia University image data which is an urban scene. The results showed the efficiency and the excellent performance of the proposed method in both terms of accuracy and computational cost.

Original languageEnglish
Title of host publication2019 10th Workshop on Hyperspectral Imaging and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2019
PublisherIEEE
Number of pages5
Volume2019-September
ISBN (Electronic)9781728152943
DOIs
Publication statusPublished - 2019
Event10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019 - Amsterdam, Netherlands
Duration: 24 Sep 201926 Sep 2019

Conference

Conference10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019
CountryNetherlands
CityAmsterdam
Period24/09/1926/09/19

Keywords

  • active learning
  • adaptive framework
  • Hyperspectral classification
  • self-learning
  • semi-supervised learning

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  • Cite this

    Jamshidpour, N., Aria, E. H., Safari, A., & Homayouni, S. (2019). Adaptive Self-Learned Active Learning Framework for Hyperspectral Classification. In 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2019 (Vol. 2019-September). [8921298] IEEE. https://doi.org/10.1109/WHISPERS.2019.8921298