Remote sensing minefield area reduction Model-based approach(3)

In the last decade, several humanitarian demining actions have acknowledged the role of remote sensing as a useful tool, able to enhance the productivity, cost-effectiveness and safety of ground-based minefield detection methods [1] [2] [3] [4]. Air- and s

capture contextual information, in terms of spatial consistency. The investigated methods involve non-causal energy-based models defined on the image lattice, hierarchical, multiresolution models defined on pyramidal representation of the image, as well as hierarchical Markovian models defined on the hierarchy of multiscale region adjacency graphs [7].

References

[1] B. Maathuis, "Remote Sensing based detection of landmine suspect areas and minefields", PhD Thesis, Department of Geosciences, University of Hamburg, 2001.

[2] S. Batman, J. Goutsias, ”Unsupervised Iterative Detection of Land Mines in Highly Cluttered Environments”, IEEE Trans. on Image Processing, vol. 12 (5), pp. 509-523, 2003.

[3] V. Pizurica, A. Katartzis, J. Cornelis, H. Sahli, "What can be expected from computerised image analysis techniques for airborne minefield detection?", 2nd International Symposium 'Operationalization of Remote Sensing', Enschede, Nederland, August 16-20,pp. 399-406, 1999.

[4] L. van Kempen, A. Katartzis, V. Pizurica, J. Cornelis, H. Sahli, "Digital signal/image processing for mine detection. Part1: Airborne approach", MINE'99, Euroconference on Sensor systems and signal processing techniques applied to the detection of mines and unexploded ordnance, Firenze, Italy, October 1-3, pp. 48-53, 1999.

[5] A. Katartzis, H. Sahli, V. Pizurica, J. Cornelis, "A Model-Based Approach to the Automatic Extraction of Linear Features from Airborne Images", IEEE Trans. on Geoscience and Remote Sensing, vol. 39 (9), pp. 2073-2079, 2001.

[6] A. Katartzis, H. Sahli, E. Nyssen , J. Cornelis, "Detection of Buildings from a Single Airborne Image using a Markov Random Field Model", IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2001), Sydney, Australia, vol. 6, pp. 2832-2834, 2001.

[7] A. Katartzis, I. Vanhamel, H. Sahli, "A Hierarchical Markovian Model for Multiscale Region-based Classification of Multispectral Images", IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data (WARDS 2003), NASA Goddard Visitor Center, Greenbelt Maryland, USA, 2003.

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