Weakly Supervised Learning for Airplane Detection in Remote Sensing Images

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)

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.

Keywords

Weakly supervised learning Negative mining Airplane detection 

Notes

Acknowledgments

This work is supported by graduate starting seed fund of Northwestern Polytechnical University under grant Z2013105.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dingwen Zhang
    • 1
  • Jianfeng Han
    • 2
  • Dahai Yu
    • 1
    • 3
  • Junwei Han
    • 1
  1. 1.School of AutomationNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Computer Department, Information Engineering SchoolTianjin University of CommerceTianjinChina
  3. 3.Tianjin Optical Electrical GaoSi Communication Engineering Technology Co., LtdTianjinChina

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