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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Tuia D, Pacifici F, Kanevski M, Emery WJ (2009) Classification of very high spatial resolution imagery using mathematical morphology and support vector machines. IEEE Trans Geosci Rem Sens 47(11):3866–3879
Deselaers T, Alexe B, Ferrari V (2012) Weakly supervised localization and learning with generic knowledge. Int J Comput Vis 100(3):275–293
Deselaers T, Alexe B, Ferrari V (2010) Localizing objects while learning their appearance. ECCV Part IV. LNCS 6314:452–466
Pandey M, Lazebnik S (2011) Scene recognition and weakly supervised object localization with deformable part-based model. ICCV, Barcelona, 6–13 November, 2011, pp. 1307–1314
Siva P, Xiang T (2011) Weakly supervised object detector learning with model drift detection. ICCV, Barcelona, 6–13 November, 2011, pp. 343–350
Achanta R, Hemami S et al (2009) Frequency-tuned salient region detection. IEEE conference on computer vision and pattern recognition, Miami, FL, 20–25 June, 2009, pp. 1597–1604
Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. IEEE conference on computer vision and pattern recognition, Minneapolis, MN, 17–22 June, 2007, pp. 1–8
Harel J, Koch C, Perona P (2007) Graph-based visual saliency. Advances in neural information processing systems. MIT Press, Cambridge, MA, pp 545–552
Judd T, Ehinger K, Durand F, Torralba A (2009) Learning to predict where humans look. Proc. IEEE 12th international conference on computer vision, Kyoto, 29 September–2 October, 2009, pp. 2106–2133.
Han B, Zhu H, Ding Y. (2011) Bottom-up saliency based on weighted sparse coding residual. ACM International Conference on Multimedia, Scottsdale, Arizona, pp. 1117–1120
Achanta R, Estrada F, Wils P, Süsstrunk S (2008) Salient region detection and segmentation. Int Conf Comput Vis Syst 5008:66–75
Siva P, Chris R, Tao X (2012) In defence of negative mining for annotating weakly labelled data. In ECCV. Springer, Berlin, pp 594–608
Acknowledgments
This work is supported by graduate starting seed fund of Northwestern Polytechnical University under grant Z2013105.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, D., Han, J., Yu, D., Han, J. (2014). Weakly Supervised Learning for Airplane Detection in Remote Sensing Images. In: Zhang, B., Mu, J., Wang, W., Liang, Q., Pi, Y. (eds) The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-319-00536-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-00536-2_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00535-5
Online ISBN: 978-3-319-00536-2
eBook Packages: EngineeringEngineering (R0)