A multiple instance neuro-fuzzy inference system for fusion of multiple landmine detection algorithms

Abstract

We present a novel method to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar (GPR). The proposed fusion method is based on a neuro-fuzzy architecture employing Multiple Instance Fuzzy Inference, called Multiple Instance Adaptive Neuro Fuzzy Inference System (MI-ANFIS). Multiple Instance Fuzzy Inference is a generalization of fuzzy inference that enables fuzzy reasoning with bags of instances. Pre-processed GPR data are usually grouped into bags of signal slices extracted at multiple depths. Labels of the bags are known, but not those of individual instances. It is very difficult to localize the objects depth automatically, and it is a very tedious process to do it manually. MI-ANFIS is capable of learning meaningful and simple fusion rules from ambiguously labeled data. Thus, making it suitable for the purpose of multiple landmine discrimination algorithms fusion. Initial testing on large and diverse GPR data collections has shown promising results. MI-ANFIS was able to overcome labeling ambiguity and outperformed other commonly used fusion methods.