Analysis of small infrared target features and learning-based false detection removal for infrared search and track

An infrared search and track system is an important research goal for military applications. Although there has been much research into small infrared target detection methods, we cannot apply them in real field situations due to the high false alarm rate caused by clutter. This paper presents a novel target attribute extraction and machine learning-based target discrimination method. In our study, eight target features were extracted and analyzed statistically. Learning-based classifiers, such as SVM and Adaboost, have been incorporated and then compared to conventional classifiers using real infrared images. In addition, the generalization capability has also been inspected for various types of infrared clutter.

[1]  Tianxu Zhang,et al.  Characteristics of contrast and application for small-target detection , 1998, Defense, Security, and Sensing.

[2]  Y L Wang,et al.  An Efficient Method of Small Targets Detection in Low SNR , 2006 .

[3]  Cordelia Schmid,et al.  Indexing Based on Scale Invariant Interest Points , 2001, ICCV.

[4]  G.-D. Wang,et al.  Facet-based infrared small target detection method , 2005 .

[5]  Tianxu Zhang,et al.  Detection of sea-surface small targets in infrared images based on multilevel filters , 1998, Other Conferences.

[6]  James R. Zeidler,et al.  Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data , 1993, IEEE Trans. Image Process..

[7]  Boris Rozovskii,et al.  Optimal nonlinear filtering for track-before-detect in IR image sequences , 1999, Optics & Photonics.

[8]  Bin Wu,et al.  Improved power-law-detector-based moving small dim target detection in infrared images , 2008 .

[9]  Xubang Shen,et al.  Architecture of a configurable 2-D adaptive filter used for small object detection and digital image processing , 2003 .

[10]  Guoyou Wang,et al.  Efficient method for multiscale small target detection from a natural scene , 1996 .

[11]  Gonzalo Pajares,et al.  Image change detection from difference image through deterministic simulated annealing , 2008, Pattern Analysis and Applications.

[12]  Leandre Sevigny,et al.  Detection of dim targets in FLIR imagery using multiscale transforms , 1994, Optics & Photonics.

[13]  Meng Hwa Er,et al.  New method for detection of dim point targets in infrared images , 1999, Optics & Photonics.

[14]  Gianluca Marsiglia,et al.  Techniques for detection of multiple, extended, and low contrast targets in infrared maritime scenarios , 2006 .

[15]  Meng Hwa Er,et al.  Max-mean and max-median filters for detection of small targets , 1999, Optics & Photonics.

[16]  Barbara Caputo,et al.  Recognition with local features: the kernel recipe , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[17]  Mohan M. Trivedi,et al.  A neural network filter to detect small targets in high clutter backgrounds , 1995, IEEE Trans. Neural Networks.

[18]  Alexander Zelinsky,et al.  Fast Radial Symmetry for Detecting Points of Interest , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Jin Liu,et al.  An algorithm based on spatial filter for infrared small target detection and its application to an all directional IRST system , 2007, International Congress on High-Speed Imaging and Photonics.

[20]  Meng Hwa Er,et al.  Multimode algorithm for detection and tracking of point targets , 1999, Defense, Security, and Sensing.

[21]  P. Duygulu,et al.  Visual categorization with bags of keypoints , 2002, eccv 2002.

[22]  Mikhail Belkin,et al.  Laplacian Support Vector Machines Trained in the Primal , 2009, J. Mach. Learn. Res..

[23]  E H Takken,et al.  LMS and matched digital filters for optical clutter suppression. , 1988, Applied optics.

[24]  Zhiguo Cao,et al.  Fast new small-target detection algorithm based on a modified partial differential equation in infrared clutter , 2007 .

[25]  Sungho Kim,et al.  Realistic infrared sequence generation by physics-based infrared target modeling for infrared search and track , 2010 .

[26]  Luis Gravano,et al.  The Stanford Digital Library metadata architecture , 1997, International Journal on Digital Libraries.

[27]  Yong Luo,et al.  Manifold Regularized Multitask Learning for Semi-Supervised Multilabel Image Classification , 2013, IEEE Transactions on Image Processing.

[28]  Antonio J. Serrano,et al.  Feature selection using ROC curves on classification problems , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[29]  Piet B. W. Schwering,et al.  Automatic detection of small surface targets with electro-optical sensors in a harbor environment , 2008, Security + Defence.

[30]  Soon Kwon,et al.  Three plot correlation-based small infrared target detection in dense sun-glint environment for infrared search and track , 2012, Defense + Commercial Sensing.

[31]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[32]  Jonathan Martin Mooney,et al.  Tracking point targets in cloud clutter , 1997, Defense, Security, and Sensing.

[33]  David Allen Langan,et al.  Spatial processing techniques for the detection of small targets in IR clutter , 1990 .

[34]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[35]  Ronda Venkateswarlu,et al.  Adaptive mean and variance filter for detection of dim point-like targets , 2002, SPIE Defense + Commercial Sensing.

[36]  Greg A. Page,et al.  Feature measurement augmentation for a dynamic programming-based IR target detection algorithm in the naval environment , 1999, Defense, Security, and Sensing.

[37]  Bo Du,et al.  Hybrid Detectors Based on Selective Endmembers , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Roger Fortin,et al.  Detection of dim targets in digital infrared imagery by morphological image processing , 1996 .

[39]  James F. Arnold,et al.  Detection and tracking of low-observable targets through dynamic programming , 1990 .

[40]  Jesmin F. Khan,et al.  Target detection in cluttered forward-looking infrared imagery , 2005 .

[41]  Jonathan Martin Mooney,et al.  Point target detection in consecutive frame staring IR imagery with evolving cloud clutter , 1995, Optics & Photonics.

[42]  Jun Yu,et al.  Complex Object Correspondence Construction in Two-Dimensional Animation , 2011, IEEE Transactions on Image Processing.

[43]  James Arnold,et al.  Detection and tracking of low-observable targets through dynamic programming , 1990, Defense + Commercial Sensing.

[44]  Subhransu Maji,et al.  Classification using intersection kernel support vector machines is efficient , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[45]  Xiangzhi Bai,et al.  Infrared small target detection and tracking under the conditions of dim target intensity and clutter background , 2007, International Symposium on Multispectral Image Processing and Pattern Recognition.

[46]  G. J. Owirka,et al.  Radar target identification using an eigen-image approach , 1994, Proceedings of 1994 IEEE National Radar Conference.

[47]  David S.K. Chan.  Unified framework for IR target detection and tracking , 1992, Defense, Security, and Sensing.

[48]  Sushil Kumar.  Detection and Tracking Algorithms for IRST , 2004 .

[49]  Frank Crosby.  Signature adaptive target detection and threshold selection for constant false alarm rate , 2005, J. Electronic Imaging.

[50]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[51]  A. N. de Jong.  IRST and its Perspective , 1995 .

[52]  LoyGareth,et al.  Fast Radial Symmetry for Detecting Points of Interest , 2003 .

[53]  Sungho Kim,et al.  Double Layered-Background Removal Filter for Detecting Small Infrared Targets in Heterogenous Backgrounds , 2011 .

[54]  Jun Yu,et al.  On Combining Multiple Features for Cartoon Character Retrieval and Clip Synthesis , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[55]  Meng Wang,et al.  Adaptive Hypergraph Learning and its Application in Image Classification , 2012, IEEE Transactions on Image Processing.

[56]  Dana H. Brooks,et al.  Point target detection in IR image sequences: a hypothesis-testing approach based on target and clutter temporal profile matching , 2000 .

[57]  I. Reed,et al.  A recursive moving-target-indication algorithm for optical image sequences , 1990 .

[58]  Meng Wang,et al.  Semisupervised Multiview Distance Metric Learning for Cartoon Synthesis , 2012, IEEE Transactions on Image Processing.

[59]  Judith Dijk,et al.  Point target detection using super-resolution reconstruction , 2007, SPIE Defense + Commercial Sensing.

[60]  Antonio Torralba,et al.  Sharing Visual Features for Multiclass and Multiview Object Detection , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[61]  Liangpei Zhang,et al.  A Multifeature Tensor for Remote-Sensing Target Recognition , 2011, IEEE Geoscience and Remote Sensing Letters.