High speed detection of aircraft targets based on proposal oriented FAST and adaptive matching of local invariant features

In this paper, a high speed detection method of aircraft targets in remote sensing images is proposed based on proposal oriented FAST and adaptive matching of local invariant features. In order to reduce the search scope, the region of parking apron is extracted by region growing based on OTSU segmentation. Moreover, Binarized Normed Gradient (BING) and Spectral Residual Saliency (SRS) are applied respectively to find useful proposals of potential aircraft targets with minor computing cost. Towards extracted proposals, the algorithm of Features from Accelerated Segment (FAST) is employed to locate key feature points precisely for various sizes of aircraft targets even very small ones. Then local invariant features characterized with well robustness against environment changes are constructed. Finally, the high speed detection algorithm of aircraft targets is implemented through adaptive matching of local invariant features with parameters adjustable accompanied by the size of aircraft targets. Comprehensive experiment results validate the well performance of our method with outstanding superiority in detection speed and accuracy for various sizes of aircraft targets.

[1]  Ali Borji,et al.  Salient Object Detection: A Benchmark , 2015, IEEE Transactions on Image Processing.

[2]  A. K. Tripathi,et al.  Shape and color features based airport runway detection , 2013, 2013 3rd IEEE International Advance Computing Conference (IACC).

[3]  Vladlen Koltun,et al.  Learning to propose objects , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[6]  Thomas Deselaers,et al.  Measuring the Objectness of Image Windows , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  S. Süsstrunk,et al.  Frequency-tuned salient region detection , 2009, CVPR 2009.

[8]  Shiming Xiang,et al.  Aircraft Detection by Deep Belief Nets , 2013, 2013 2nd IAPR Asian Conference on Pattern Recognition.

[9]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Philip H. S. Torr,et al.  BING: Binarized normed gradients for objectness estimation at 300fps , 2014, Computational Visual Media.

[11]  Xian Sun,et al.  Object Detection in High-Resolution Remote Sensing Images Using Rotation Invariant Parts Based Model , 2014, IEEE Geoscience and Remote Sensing Letters.

[12]  Darius Burschka,et al.  Adaptive and Generic Corner Detection Based on the Accelerated Segment Test , 2010, ECCV.

[13]  Xin Wang,et al.  Airport Detection in Remote Sensing Images Based on Visual Attention , 2011, ICONIP.

[14]  Bernt Schiele,et al.  What Makes for Effective Detection Proposals? , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Hongping Cai,et al.  Airplane detection in remote sensing image with a circle-frequency filter , 2005, International Conference on Space Information Technology.