CSIE 2011: Advanced Research on Computer Science and Information Engineering pp 327-333 | Cite as
Saliency-Based Automatic Target Detection in Remote Sensing Images
Abstract
Automatic target detection in satellite images remains a challenging problem. Previous methods mainly focus on independent detection of multiple targets. In this paper, we propose a simultaneous multi-class target detection approach by using saliency computation. The advantages are twofold. First, saliency map is computed only once for all target types. This saves a large amount of computational time but does not miss any targets. Second, we use small regions, obtained from over-segmentation, to be the elementary unit of detection. This provides shape information to remove most false candidates for the final detection. Experiments show that the targets can be quickly detected and the detection rate is as high as the independent detecting methods.
Keywords
Salient Object Salient Region Shape Match Remote Sensing Image Automatic Target RecognitionPreview
Unable to display preview. Download preview PDF.
References
- 1.Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–518 (2001)Google Scholar
- 2.Yao, J., Zhang, Z.F.: Object detection in aerial imagery based on enhanced semi-supervised learning. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1012–1017 (2005)Google Scholar
- 3.Olson, C.F., Huttenlocher, D.P.: Automatic target recognition by matching oriented edge pixels. IEEE Transactions Image Processing 6(1), 103–113 (1997)CrossRefGoogle Scholar
- 4.Chalmond, B., Francesconi, B., Herbin, S.: Using hidden scale for salient object detection. IEEE Transactions Image Processing 15, 2644–2656 (2006)CrossRefGoogle Scholar
- 5.Guo, C.L., Ma, Q., Zhang, L.M.: Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
- 6.Comaniciu, D., Meer, P., Member, S.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)CrossRefGoogle Scholar
- 7.Steger, C.: An unbiased detector of curvilinear structures. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 113–125 (1998)CrossRefGoogle Scholar
- 8.Xu, L., Oja, E., Kultanen, P.: A new curve detection method: Randomized hough transform. Pattern Recognition Letter 11(5), 331–338 (1990)CrossRefMATHGoogle Scholar
- 9.Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape context. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(24), 509–522 (2002)CrossRefGoogle Scholar