Remote Sensing For Area Reduction Of Minefield Suspect Areas

Author : Nada Milisavljevic
Publisher :
ISBN 13 : 9783902613110
Total Pages : pages
Book Rating : 4.6/5 (131 download)

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Download or read book Multi-sensor Data Fusion Based on Belief Functions and Possibility Theory: Close Range Antipersonnel Mine Detection and Remote Sensing Mined Area Reduction written by Nada Milisavljevic and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Several fusion methods for close range humanitarian mine detection and remote sensing mined area reduction are presented and compared. These methods are based on the belief functions as well as on the fuzzy/possibility theory. Regarding close range mine detection, the differences at the combination step are mainly highlighted in this comparison. The modeling step is performed according to the semantics of each framework, but the designed functions are as similar as possible, so as to enhance the combination step. Different fusion operators are tested, depending on the information and its characteristics. An appropriate modeling of the data along with their combination in a possibilistic framework leads to a better differentiation between mines and friendly objects. The decision rule is designed to detect all mines, at the price of a few confusions with friendly objects. This is a requirement of this sensitive application domain (mines must not be missed). Still the number of false alarms remains limited in our results. The robustness of the choice of the operator is also tested, and all mines are detected for all fusion schemes. The proposed modeling is flexible enough to be easily adapted to the introduction of new pieces of information about the types of objects and their characteristics, as well as of new sensors. As far as remote sensing mined area reduction is concerned, the concept of the whole method is described, developed within the SMART project, and most of the attention is given to the data fusion task. The proposed fusion approaches are to a large part original and constitute by themselves a result of the project. Results have been obtained on three test sites in Croatia, being representative of the South Eastern Europe, with the three most promising approaches, and as an example, fusion results for one of the sites are given here. Note that in order to apply the proposed methodology in another context, a new field campaign would be needed to derive and implement new general rules. We have shown how the results can be improved by introducing additional knowledge in the fusion process. A spatial regularization at a regional level further improves the results. At the end, the results are at least as good as the ones provided for each class by the best classifier for that class. Therefore they are globally better than any input classifier or detector. This shows the improvement brought by fusion. The user has the possibility to be involved in the choice of the classifiers, in the choice of some of the parameters (in particular for the fuzzy fusion approach, some supervision is still.