Weakly Supervised Learning for Airplane Detection in Remote Sensing Images
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.
KeywordsWeakly supervised learning Negative mining Airplane detection
This work is supported by graduate starting seed fund of Northwestern Polytechnical University under grant Z2013105.
- 3.Deselaers T, Alexe B, Ferrari V (2010) Localizing objects while learning their appearance. ECCV Part IV. LNCS 6314:452–466Google Scholar
- 4.Pandey M, Lazebnik S (2011) Scene recognition and weakly supervised object localization with deformable part-based model. ICCV, Barcelona, 6–13 November, 2011, pp. 1307–1314Google Scholar
- 5.Siva P, Xiang T (2011) Weakly supervised object detector learning with model drift detection. ICCV, Barcelona, 6–13 November, 2011, pp. 343–350Google Scholar
- 6.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. 1597–1604Google Scholar
- 7.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. 1–8Google Scholar
- 8.Harel J, Koch C, Perona P (2007) Graph-based visual saliency. Advances in neural information processing systems. MIT Press, Cambridge, MA, pp 545–552Google Scholar
- 9.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.Google Scholar
- 10.Han B, Zhu H, Ding Y. (2011) Bottom-up saliency based on weighted sparse coding residual. ACM International Conference on Multimedia, Scottsdale, Arizona, pp. 1117–1120Google Scholar
- 12.Siva P, Chris R, Tao X (2012) In defence of negative mining for annotating weakly labelled data. In ECCV. Springer, Berlin, pp 594–608Google Scholar