Detecting and locating landmine fields from vehicle- and air-borne measured IR Images

Abstract Air- and vehicle-borne sensor-based technique is a potentially attractive approach for fast detecting landmines and locating landmine fields towards humanitarian demining. For images measured from airborne and vehicle-borne cameras, landmines may be indicated by direct or indirect signs, e.g., spatial difference from their surroundings due to digging or, due to thermal and material signatures. The background in images usually consists of various types of noise and clutter, e.g., thermal noise, sand, gravel road and vegetation, thus making the detection even more difficult. This paper is focused on the following aspects: (1) Finding a robust detector that is suitable for detecting/locating landmine candidates and man-made landmarks by using infrared images measured from vehicle- or air-borne sensors; (2) Interpreting the detector using the 2D isotropic bandpass filter, matched filter, detection theory and thermodynamic-based landmine models; (3) Extending the detector to a multiscale version where landmine detectability is enhanced by automatically selecting a proper scale and localization is improved by inter-scale position tracing. We propose a special type of isotropic feature detector that exploits the characteristic difference between landmines and their surroundings in the spatial-frequency domain under the multiscale framework. Experiments were performed on several infrared images measured from vehicle-borne sensors as well as airborne sensors on a helicopter over the test bed scenarios. The performance of the detector was also evaluated in terms of detectability, localization, and automatic scale selection of the detector. These results and evaluations have shown the effectiveness of the method and its potential in landmine field detection.

[1]  Irene Yu-Hua Gu,et al.  Corner-Based Curve Feature Extraction for Object Retrieval , 1999 .

[2]  Karl Rohr,et al.  Extraction of 3d anatomical point landmarks based on invariance principles , 1999, Pattern Recognit..

[3]  Han Wang,et al.  Real-time corner detection algorithm for motion estimation , 1995, Image Vis. Comput..

[4]  Mark Hedley,et al.  Fast corner detection , 1998, Image Vis. Comput..

[5]  Farzin Mokhtarian,et al.  Robust Image Corner Detection Through Curvature Scale Space , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Rama Chellappa,et al.  A new approach to image feature detection with applications , 1996, Pattern Recognit..

[7]  Arthur Filippidis,et al.  Using genetic algorithms and neural networks for surface land mine detection , 1999, IEEE Trans. Signal Process..

[8]  Brian A. Baertlein,et al.  Improving detection of buried land mines through sensor fusion , 1998, Defense, Security, and Sensing.

[9]  Andreas Antoniou,et al.  Two-Dimensional Digital Filters , 2020 .

[10]  Laxmi Parida,et al.  Junctions: Detection, Classification, and Reconstruction , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  R. Viswanathan,et al.  An introduction to statistical signal processing with applications , 1979 .

[12]  P. K. Bishop,et al.  Airborne minefield detection , 1998 .

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

[14]  A.-L. Christiansen,et al.  Optical mine reconnaissance at the National Defence Research Establishment. Multispectral imaging and classification: thermodynamic soil modelling , 1996 .

[15]  Song-Chun Zhu,et al.  Stochastic Jump-Diffusion Process for Computing Medial Axes in Markov Random Fields , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Song-Chun Zhu,et al.  Exploring Texture Ensembles by Efficient Markov Chain Monte Carlo-Toward a 'Trichromacy' Theory of Texture , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Stephen M. Smith,et al.  SUSAN—A New Approach to Low Level Image Processing , 1997, International Journal of Computer Vision.

[18]  S. L. Earp,et al.  Ultra-wideband ground-penetrating radar for the detection of buried metallic mines , 1996, Proceedings of the 1996 IEEE National Radar Conference.

[19]  Irene Yu-Hua Gu,et al.  3D matched filter for detection of land mines using spatio-temporal thermal modeling , 2000, Defense, Security, and Sensing.

[20]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

[21]  Patrick Flandrin,et al.  Time-Frequency/Time-Scale Analysis , 1998 .

[22]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.