Weakly supervised learning for airplane detection in remote sensing images

Dingwen Zhang, Jianfeng Han, Dahai Yu, Junwei Han

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In contrast to the conventional approaches to learn geo-target classifier using fully supervised learning techniques which heavily rely on the artificial annotation in the training set of remote sensing images (RSIs), this paper attempts to develop a weakly supervised learning (WSL) approach for airplane detection in RSIs with cluttered background. The framework includes a novel WSL method to train airplane classifier using the training images with weak labels and an efficient detection scheme to localize the airplanes. The proposed WSL mainly consists of three components: the negative mining based training set initialization, the updating process for both the positive and negative training set, and the classifier evaluation mechanism that can efficiently terminate the updating process for the best performance. Comprehensive experiments on a large number of RSIs and comparisons with state-of-the-art fully supervised models demonstrate the effectiveness and efficiency of the proposed work.

Original languageEnglish
Title of host publicationProceedings of the Second International Conference on Communications, Signal Processing, and Systems, CSPS 2013
PublisherSpringer Verlag
Pages155-163
Number of pages9
ISBN (Print)9783319005355
DOIs
StatePublished - 2014
Event2nd International Conference on Communications, Signal Processing, and Systems, CSPS 2013 - Tianjin, China
Duration: 1 Sep 20132 Sep 2013

Publication series

NameLecture Notes in Electrical Engineering
Volume246 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference2nd International Conference on Communications, Signal Processing, and Systems, CSPS 2013
Country/TerritoryChina
CityTianjin
Period1/09/132/09/13

Keywords

  • Airplane detection
  • Negative mining
  • Weakly supervised learning

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