Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated by OKTAL-SE.

[1]  Samuel Cheng,et al.  Image registration using BP-SIFT , 2013, J. Vis. Commun. Image Represent..

[2]  Jordi Inglada,et al.  Automatic Fusion of SAR and Optical Imagery based on Line Features , 2008 .

[3]  Maria T. Rey,et al.  Fusion of optical, infrared, and radar imagery for coastal surveillance , 1996, Defense, Security, and Sensing.

[4]  Sridhar Srinivasan,et al.  An Active Vision Based SAR-FLIR Fusion ATR System for Detection and Recognition of Ground Targets , 1996 .

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

[6]  Sungho Kim.  Analysis of small infrared target features and learning-based false detection removal for infrared search and track , 2013, Pattern Analysis and Applications.

[7]  Li Xiang,et al.  Ground targets detection and tracking based on integrated information in infrared images , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[8]  Lance Kaplan,et al.  Improved SAR target detection via extended fractal features , 2001 .

[9]  Xiaogang Wang,et al.  DeepID-Net: Deformable deep convolutional neural networks for object detection , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Taek Lyul Song,et al.  Automatic target recognition and tracking in forward-looking infrared image sequences with a complex background , 2013 .

[11]  Erik Blasch,et al.  Predicted radar/optical feature fusion gains for target identification , 2010, Proceedings of the IEEE 2010 National Aerospace & Electronics Conference.

[12]  Neeraj Pujara,et al.  Portable scalable architecture for model-based FLIR ATR and SAR/FLIR fusion , 1999, Defense, Security, and Sensing.

[13]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[14]  G. B. Goldstein,et al.  False-Alarm Regulation in Log-Normal and Weibull Clutter , 1973, IEEE Transactions on Aerospace and Electronic Systems.

[15]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[16]  Alfred O. Hero,et al.  Robust detection of SAR/IR targets via invariance , 1997, Proceedings of International Conference on Image Processing.

[17]  Gui Gao,et al.  Statistical Modeling of SAR Images: A Survey , 2010, Sensors.

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

[19]  Maria Huhtala,et al.  Random Variables and Stochastic Processes , 2021, Matrix and Tensor Decompositions in Signal Processing.

[20]  Sungho Kim,et al.  Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track , 2012, Pattern Recognit..

[21]  Harold Pashler,et al.  A Boolean map theory of visual attention. , 2007, Psychological review.

[22]  Sungho Kim.  High-Speed Incoming Infrared Target Detection by Fusion of Spatial and Temporal Detectors , 2015, Sensors.

[23]  K. Khoshelhama,et al.  A COMPARISON OF BAYESIAN AND EVIDENCE-BASED FUSION METHODS FOR AUTOMATED BUILDING DETECTION IN AERIAL DATA , 2008 .

[24]  Bernard F. Buxton,et al.  Evolving edge detectors with genetic programming , 1996 .

[25]  Nicholas J. Redding,et al.  Comparison of selected features for target detection in synthetic aperture radar imagery , 1999, Conference Record of the Thirty-Third Asilomar Conference on Signals, Systems, and Computers (Cat. No.CH37020).

[26]  J. Susaki,et al.  MULTI-TEMPORAL SAR AND OPTICAL DATA FUSION WITH TEXTURE MEASURES FOR LAND COVER CLASSIFICATION BASED ON THE BAYESIAN THEORY , 2001 .

[27]  Lawrence A. Klein,et al.  Sensor and Data Fusion: A Tool for Information Assessment and Decision Making , 2004 .

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

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

[30]  Thierry Cathala,et al.  Simulation of active and passive infrared images using the SE-WORKBENCH , 2007, SPIE Defense + Commercial Sensing.

[31]  S. Iyengar,et al.  Multi-Sensor Fusion: Fundamentals and Applications With Software , 1997 .

[32]  Mark J. T. Smith,et al.  Multistage algorithm for detection of targets in SAR image data , 1997, Defense, Security, and Sensing.

[33]  Jean-Marc Boucher,et al.  Multisource classification using ICM and Dempster-Shafer theory , 2002, IEEE Trans. Instrum. Meas..

[34]  Neeraj Pujara,et al.  Transition from lab to flight demo for model-based FLIR ATR and SAR-FLIR fusion , 2000, SPIE Defense + Commercial Sensing.

[35]  Celine Tison,et al.  Combining SAR and optical features in a SVM classifier for man-made structures detection , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

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

[37]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

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

[39]  Wenxing Ye,et al.  Target detection for very high-frequency synthetic aperture radar ground surveillance , 2012 .

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

[41]  Kamal Jamshidi,et al.  Small object detection in cluttered image using a correlation based active contour model , 2012, Pattern Recognit. Lett..

[42]  Xiaoli Yu,et al.  Analysis of hyperspectral infrared and low-frequency SAR data for target classification , 1996, Defense, Security, and Sensing.

[43]  Leslie M. Novak,et al.  On the performance of order-statistics CFAR detectors , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[44]  Yusuf Soyman.  Robust automatic target recognition in FLIR imagery , 2012, Defense + Commercial Sensing.

[45]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[46]  Yang Chen.  Registration of SAR and FLIR images for ATR applications , 2001 .

[47]  Nikola S. Subotic,et al.  Multiresolution detection of coherent radar targets , 1997, IEEE Trans. Image Process..

[48]  Yi Su,et al.  Feature-based classification fusion of vehicles in high-resolution SAR and optical imagery , 2005, International Symposium on Multispectral Image Processing and Pattern Recognition.

[49]  Lan-Rong Dung,et al.  Implementation of RANSAC Algorithm for Feature-Based Image Registration , 2013 .

[50]  Xiaogang Wang,et al.  Deep Learning Strong Parts for Pedestrian Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[51]  Rama Chellappa,et al.  Non-Gaussian CFAR techniques for target detection in high resolution SAR images , 1994, Proceedings of 1st International Conference on Image Processing.

[52]  Qiuchan Bai,et al.  Image Fusion and Recognition based on Compressed Sensing Theory , 2015 .

[53]  Thierry Cathala,et al.  New impressive capabilities of SE-workbench for EO/IR real-time rendering of animated scenarios including flares , 2015, SPIE Security + Defence.

[54]  Jie Ma,et al.  Robust method for infrared small-target detection based on Boolean map visual theory. , 2014, Applied optics.