BgCut: Automatic Ship Detection from UAV Images

Ship detection in static UAV aerial images is a fundamental challenge in sea target detection and precise positioning. In this paper, an improved universal background model based on Grabcut algorithm is proposed to segment foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches.

[1]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[2]  Vladimir Kolmogorov,et al.  "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..

[3]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[4]  Michael F. Cohen,et al.  An iterative optimization approach for unified image segmentation and matting , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[5]  Pascal Vasseur,et al.  A Vision Algorithm for Dynamic Detection of Moving Vehicles with a UAV , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[6]  Rachid Deriche,et al.  A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape , 2007, International Journal of Computer Vision.

[7]  Yijun He,et al.  Ship detection with the fuzzy c-mean clustering algorithm using fully polarimetric SAR , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[8]  Carlos López-Martínez,et al.  Advances in unsupervised ship detection with multiscale techniques , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[9]  Fátima N. S. de Medeiros,et al.  Target Detection in SAR Images Based on a Level Set Approach , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[10]  Albert Rango,et al.  Texture and Scale in Object-Based Analysis of Subdecimeter Resolution Unmanned Aerial Vehicle (UAV) Imagery , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Huiyan Liu.  Fast Target Detection for SAR Images Based on Weighted Parzen-Window Clustering Algorithm , 2010, 2010 International Conference on Communications and Intelligence Information Security.

[12]  Albert Rango,et al.  Change Detection using 75-year Aerial Photo and Satellite Data Sets, Inexpensive Means to Obtain 6 cm Resolution Data, and Developing Opportunities for Community-oriented Remote Sensing through Photography , 2010 .

[13]  Bo Zhang,et al.  Ship detection based on compound distribution with Synthetic Aperture Radar images , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[14]  Hong Zhang,et al.  Adaptive shape prior in graph cut segmentation , 2010, 2010 IEEE International Conference on Image Processing.

[15]  Lam.  A Study on Similarity Computations in Template Matching Technique for Identity Verification , 2010 .

[16]  Gerard Margarit,et al.  Ship Classification in Single-Pol SAR Images Based on Fuzzy Logic , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Yuan Tian,et al.  Ship Detection Using Texture Statistics from Optical Satellite Images , 2011, 2011 International Conference on Digital Image Computing: Techniques and Applications.

[18]  Koen E. A. van de Sande,et al.  Segmentation as selective search for object recognition , 2011, 2011 International Conference on Computer Vision.

[19]  Peng Zhang,et al.  Unsupervised multi-class segmentation of SAR images using triplet Markov fields models based on edge penalty , 2011, Pattern Recognit. Lett..

[20]  T. Intharah,et al.  MuralCut: Automatic character segmentation from mural images , 2012, 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[21]  Kilian M. Pohl,et al.  Segmentation of myocardium using deformable regions and graph cuts , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[22]  Bo Zhang,et al.  Ship detection based on feature confidence for high resolution SAR images , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[23]  Huanxin Zou,et al.  A CFAR Detection Algorithm for Generalized Gamma Distributed Background in High-Resolution SAR Images , 2013, IEEE Geoscience and Remote Sensing Letters.

[24]  Çaglar Senaras,et al.  Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Xiaohua Zhang,et al.  A Novel SAR Image Change Detection Based on Graph-Cut and Generalized Gaussian Model , 2013, IEEE Geoscience and Remote Sensing Letters.

[26]  Gajendra Singh Chandel,et al.  Analysis of Image Segmentation Algorithms Using MATLAB , 2013 .