In this paper, a decision level fusion using multiple pre-screener algorithms is proposed for the detection of buried
landmines from Ground Penetrating Radar (GPR) data. The Kernel Least Mean Square (KLMS) and the Blob Filter
pre-screeners are fused together to work in real time with less false alarms and higher true detection rates. The effect
of the kernel variance is investigated for the KLMS algorithm. Also, the results of the KLMS and KLMS+Blob filter
algorithms are compared to the LMS method in terms of processing time and false alarm rates. Proposed algorithm is
tested on both simulated data and real data collected at the field of IPA Defence at METU, Ankara, Turkey.
Proc. SPIE. 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX
KEYWORDS: Target detection, Long wavelength infrared, Detection and tracking algorithms, Cameras, Feature extraction, Infrared radiation, Forward looking infrared, Ground penetrating radar, Land mines, General packet radio service
In this paper, we propose a system to detect buried disk-shaped landmines from ground penetrating radar (GPR) and forward-looking long wave infrared (FL-LWIR) data. The data is collected from a test area of 500m<sup>2</sup>, which was prepared at the IPA Defence, Ankara, Turkey. This test area was divided into four lanes, each of size 25m length by 4m width and 1m depth. Each lane was first carefully cleaned of stones and clutter and then filled with different soil types, namely fine-medium sand, course sand, sandy silt loam and loam mix. In all lanes, various clutter objects and landmines were buried at different depths and at 1meter intervals. In the proposed approach, IR data is used as a pre-screener. Then possible target regions are further analyzed using the GPR data. IR data processing is done in three steps such as preprocessing, target detection, and postprocessing. In the pre-processing stage, bilateral noise reduction filtering is performed. The target detection stage finds circular targets by a radial transformation algorithm. The proposed approach is compared with the RX algorithm used widely for anomaly detection. The suspicious regions are further analyzed using Histogram of Oriented Gradient (HOG) features that are extracted from GPR images and classified by SVM. The same approach can also be applied in a parallel way where the results are combined using decision level fusion. The results of the proposed approach are given on different scenarios including different weather temperature and depth of buried targets.