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.
Estimating the number of endmembers and their spectrum is a challenging task. For one, endmember detection algorithms may over or underestimate the number of endmembers in a given scene. Further, even if the number of endmembers are known beforehand, result of the endmember detection algorithms may not be accurate. They may find multiple endmembers representing the same class, while completely missing some of the endmembers representing the other classes. This hinders the performance of unmixing, resulting in incorrect endmember proportion estimates. In this study, SHARE-2012 AVON data pertaining to the unmixing experiment was considered. It was cropped to include only the eight pieces of cloth and a portion of the surrounding asphalt and grass. This data was used to evaluate the performance of five endmember detection algorithms, namely the PPI, VCA, N-FINDR, ICE and SPICE; none of which found the endmember spectra correctly. All of these algorithms generated multiple endmembers corresponding to the same class or they completely missed some of the endmembers. Hence, the peak-aware N-FINDR algorithm was devised to group the endmembers of the same class so as not to over or under-estimate the true endmembers. The comparisons with or without this refinement for the N-FINDR algorithm are demonstrated.
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.
In hyperspectral imaging, shadowy areas present a major problem as targets in shadow show decreased or no spectral signatures. One way to mitigate this problem is by the fusion of hyperspectral data with LiDAR data; since LiDAR data presents excellent information by providing elevation information, which can then be used to identify the regions of shadow. Although there is a lot of work to detect the shadowy areas, many are restricted to distinct platforms like ARGCIS, ENVI etc. The purpose of this study is to (i) detect the shadow areas and to (ii) give a shadowiness scale in LiDAR data with Matlab in an efficient way. For this work, we designed our Line of Sight (LoS) algorithm that is optimized to run in a Matlab interface. The LoS algorithm uses the sun angles (altitude and azimuth) and elevation of the earth; and marks the pixel as “in shadow” if there lies an object of higher elevation between a given pixel and the sun. This is computed for all pixels in the scene and a shadow map is generated. Further, if a pixel is marked as a shadow area, the algorithm assigns a different darkness level which is inversely proportional to the distance between the current pixel and the object that causes the shadow. With this shadow scale, it is both visually and computationally possible to distinguish the soft shadows from the dark shadows; an important information for hyperspectral imagery. The algorithm has been tested on the SHARE 2012 Avon AM dataset. We also show the effect of the shadowiness scale on the spectral signatures.
Proc. SPIE. 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII
KEYWORDS: Detection and tracking algorithms, Data modeling, Image segmentation, Algorithm development, Optimization (mathematics), Statistical modeling, Expectation maximization algorithms, Ground penetrating radar, Land mines, General packet radio service
Multiple instance learning is a recently researched learning paradigm in machine intelligence which operates under conditions of uncertainty. A Multiple Instance Hidden Markov Model (MI-HMM) is investigated with applications to landmine detection using ground penetrating radar data. Without introducing any additional parameters, the MI-HMM provides an elegant and simple way to learn the parameters of an HMM in a multiple instance framework. The efficacy of the model is shown on a real landmine dataset. Experiments on the landmine dataset show that MI-HMM learning is effective.
Recent terrorist attacks have sprung a need for a large scale explosive detector. Our group has developed
differential reflection spectroscopy which can detect explosive residue on surfaces such as parcel, cargo and
luggage. In short, broad band ultra-violet and visible light is shone onto a material (such as a parcel) moving on
a conveyor belt. Upon reflection off the surface, the light intensity is recorded with a spectrograph (spectrometer
in combination with a CCD camera). This reflected light intensity is then subtracted and normalized with
the next data point collected, resulting in differential reflection spectra in the 200-500 nm range. Explosives
show spectral finger-prints at specific wavelengths, for example, the spectrum of 2,4,6, trinitrotoluene (TNT)
shows an absorption edge at 420 nm. Additionally, we have developed an automated software which detects the
characteristic features of explosives. One of the biggest challenges for the algorithm is to reach a practical limit
of detection. In this study, we introduce our automatic detection software which is a combination of principal
component analysis and support vector machines. Finally we present the sensitivity and selectivity response of
our algorithm as a function of the amount of explosive detected on a given surface.