Presently, the number of landmines planted around the world totalizes more than 110 million and, far from slowing down,
the landmine production planting rate is, at least, one order of magnitude higher than the rate at which they are removed.
In this work a technique to detect buried landmines using boundary detection in IR images, is presented. The buried
objects have different temperature than the surrounding soil. We find the object contours by means of an algorithm of
B-Spline deformable curves.
Under a statistical model, regions with different temperatures can be characterized by the values of the statistical
parameters of these distributions. Therefore, this information can be used to find boundaries among different regions in the
image.
The B-Spline approach has been widely used in curve representation for boundary detection, shape approximation,
object tracking and contour detection. Contours formulated by means of B-Splines allow local control, require few parameters
and are intrinsically smooth. The algorithm consists in estimating the parameters along lines strategically disposed
on the image. The true boundary is found when the values of these parameters vary abruptly on both sides. A likelihood
function is maximized to determine the position of such boundaries.
We present the experimental results, which show the behavior of the detection method, according to the buried object
depth and the elapsed time from the cooling initial time. The obtained results exhibit that it is possible to recognize the
shape of the objects, buried at different depths, with a low computational effort.
A great development of technologies for the detection of buried landmines took place worldwide in the last years. In
Argentina, a project for the development of an autonomous robot with sensors for landmines detection was recently
approved by the Science and Technology National Agency. Within this project we are studying the detection of
landmines by infrared radiation.
Metallic and plastic objects with landmines shape and dimension were buried at different depths from 1 to 4 cm in soil
and sand. Periodic natural warming by solar radiation or artificial warming by means of electric resistances or flash
lamps were applied. Infrared images were obtained in the 8-12 micrometers spectral band with a microbolometer
camera. The IR images were processed by different methods to obtain a definition as good as possible of the buried
objects. After this a B-Spline method was applied to detect the targets contours and determine shape and dimensions of
them so as to distinguish landmines from other objects.
We are looking for a landmine detection method as simple and fast possible, with detection capability of metallic and
plastic landmines and an acceptable false alarm rate which would be reduced when applied with other detection
methods as GPR and electromagnetic induction.
We present obtained and processed images and results obtained to distinguish buried landmines from other buried objects.
A great development of technologies for the detection of buried objects took place in the last years. Applications in archeology, finding of pipe lines and others were important, but most attention was paid in humanitarian detection of land mines and unexploded ordnances. Among these technologies, thermography is one of the most useful techniques and has been applied concurrent with other ones (Ground Penetrating Radar, Electromagnetic Induction, etc.) We have made several experiments to obtain thermographic images of buried objects in the middle and far infrared, in laboratory and in field, and in different types of terrain: naked ground, ground covered with grass and sand. We employed, as warming methods, natural sun radiation and blowing of warm air or halogen lamps. We have used metallic and dielectric objects of different sizes and shapes so as to recognize them by their characteristics. The acquired images were improved using noise reduction and image enhancement techniques.
In this work we present the thermographic images obtained. All measurements were made at short distance, less than 100 cm, as the objective of our work is to develop a thermographic imaging system for the detection of buried objects to be installed in an autonomous ground robot.
There are many statistical models for Synthetic Aperture Radar (SAR) images. Among them, the multiplicative model is based on the assumption that the observed random field Z is the result of the product of two independent and unobserved random fields: X and Y. The random field X models the backscatter, and thus depends only on the type of area each pixel belongs to. On the other hand, the random field Y takes into account that SAR images are the result of a coherent imaging system that produces the well known phenomenon called speckle, and that they are generated by performing an average of n statistically independent images - looks- in order to reduce the speckle effect. There are various ways of modeling the random fields X and Y. Recently Frery et. al. proposed the distributions (Gamma) 1/2 ((alpha) ,(gamma) ) and (Gamma) 1/2(n,n) for of X and Y respectively. This resulted in a new distribution for Z: the G0A((alpha) ,(gamma) ,n) distribution. Here, the parameters (alpha) and (gamma) depend on the ground truth of each pixel and the parameter n is the number of looks used to generate the image. The advantage of this distribution over the ones used in the past is that it models very well extremely heterogenous areas like cities, as well as moderately heterogeneous areas like forests, and homogeneous areas like pastures. As the ground truth can be characterized by the parameters (alpha) and (gamma) , their estimation for each pixel generates parameter maps that can be used as the input for classical classification methods. In this work, different parameter estimation procedures are used and compared on synthetic and real SAR images, and then, supervised and unsupervised classifications are performed and evaluated.
The multiplicative model has been widely used to explain the statistical properties of SAR images. In it, the model for the image Z is a 2D random field, that is regarded as the result of the product of X, the backscatter that depends on the physical characteristics of the sensed area, and Y, the speckle that depends on the number of looks used to generate the image Z. The most famous distribution for SAR images based on the multiplicative model is the K distribution (Jackeman et al). Recently Frery et al. proposed an alternative distribution, the G0A((alpha) ,(gamma) ,n) distribution which models very well extremely heterogenous areas (cities) as well as moderately heterogeneous areas (forest) and homogeneous areas (crop fields). The ground truth at each pixel can be characterized by the statistical parameters (alpha) and (gamma) , while n is constant for all of the pixels. The purpose of estimating these parameters for every pixel is twofold: first, it can be used to perform a segmentation process and, second, it can be used for gray level restoration. In this work we follow a Markov random field approach and propose an energy function derived from the statistical model adopted: G0A((alpha) ,(gamma) ,n). Edge- preservation is taken into account implicitly in the energy function.
The present work is a report of the results obtained using M-estimators, Mahalanobis distance and openings and closings in supervised segmentation of multiband SAR images. Once the training zones on the SAR images are established, the M- estimators robust estimation method is used to determine the mean value and the covariance matrix for each segment. Then, to carry out the first stage in the segmentation process, the Mahalanobis distance is used. To improve the segmentation obtained in the previous stage, a sequence of openings and closing s with a structuring element of growing size is applied. Real and synthetic images were used to evaluate the results.
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