Abstract
Recently, ground-penetrating radar (GPR) has been extended as a well-known area to investigate the subsurface objects. However, its output has a low resolution, and it needs more processing for more interpretation. This paper presents two algorithms for landmine detection from GPR images. The first algorithm depends on a multi-scale technique. A Gaussian kernel with a particular scale is convolved with the image, and after that, two gradients are estimated; horizontal and vertical gradients. Then, histogram and cumulative histogram are estimated for the overall gradient image. The bin values on the cumulative histogram are used for discrimination between images with and without landmines. Moreover, a neural classifier is used to classify images with cumulative histograms as feature vectors. The second algorithm is based on scale-space analysis with the number of speeded-up robust feature (SURF) points as the key parameter for classification. In addition, this paper presents a framework for size reduction of GPR images based on decimation for efficient storage. The further classification steps can be performed on images after interpolation. The sensitivity of classification accuracy to the interpolation process is studied in detail.
Highlights
The cumulative histogram is estimated for each case
Instead of using a thresholding process for the detection of landmines, we can train a neural classifier with specific bins of the cumulative histograms of gradients for images with and without landmines
We can conclude that the speeded-up robust feature (SURF)-based classification is more powerful than the technique based on the cumulative histogram of gradients
Summary
Landmines are explosive objects buried at certain depths underground. Landmine detection is very vital for the efforts of re-cultivation and civilization. The authors developed a landmine detection technique based on extracting cepstral features from the GPR images and using neural classification. This technique is simple, it destroys the 2-D nature of images and the 2-D geometry of objects. Hamdi and Figui [6] presented a GPR landmine detection technique based on an ensemble hidden Markov model They proved that the different textures of landmine activities are reflected in their model parameters. The GPR is one of the most widely-used electromagnetic techniques for landmine detection due to its advantages compared to other tools, as it is simple and can be used The GPR is used in several applications like studying bedrocks, soil types, roads, mapping of archaeological features, and landmine detection [14]
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