A review of satellite and airborne sensors for remote sensing based detection of minefields and landmines

This paper focuses on the use of space and airborne sensors that can be applied to detect landmines and minefields. First the landmine and minefield problem is addressed and examples of the use of remote sensing images are presented that could provide valuable information for the mine action process and assist in conventional minefield and landmine detection methods. This is followed by an overview on relevant (declassified) aspects related to strategic overhead detection techniques developed by the military/intelligence community as well as those of civilian space and airborne remote sensing programmes. The airborne sensing techniques describe the state of the art of sensors such as optical (film, multi- and hyperspectral sensors), thermal infrared as well as microwave sensors and their suitability--limitations for remote sensing based minefield and landmine detection purposes.     

The impact of false alarm mitigation on surface landmine detection in MWIR imagery

Surface landmine and minefield detection from airborne imagery is a difficult problem. As part of the minefield detection process, anomaly detection is performed to identify potential landmines in individual airborne images. Post-processing is performed on the initial landmines identified to reduce the number of false alarms, referred to as false alarm mitigation. In this research, a circular harmonics transform image processing approach (the CHT method) and a constant false alarm rate technique (the RX approach) are investigated for surface landmine detection and false alarm mitigation in medium wave infrared (MWIR) image data. The false alarm mitigation approach integrates the CHT and RX methods to identify candidate landmine locations with one technique at a given false alarm rate and applies the other technique to confirm landmine locations and eliminate potential false alarms. Individual detector and false alarm mitigation experimental results are presented for 31 daytime and 43 nighttime MWIR images containing 76 and 142 landmines, respectively. At a 0.9 desired probability of landmine detection, experimental results show that false alarm mitigation reduces the false alarm rate by as much as 84.3% and 13.7% for daytime and nighttime images, respectively, maintaining the probability of detection at 0.85 and 0.90, respectively.     

Multiresolution feature detection using a family of isotropicbandpass filters

Presents a novel method which uses a special type of multi-scale isotropic band-pass filters to detect image landmark and corner features. This paper mainly contributes to the solutions of the following problems: (1) defining a general family of feature detectors under a unified framework that are able to enhance and detect the desired features; (2) explaining theoretically and experimentally why these feature points can be detected by the proposed detectors; and (3) extending the detectors to multi-scale versions for jointly achieving good detectability and localization where an automatic scale selection method is applied. The paper then presents several applications for detecting landmarks and corners using the proposed methods, in order to illustrate their usage. These include detecting landmarks from gesture images (of the face and hand), from airborne and vehicle-borne IR landmine images, and from images containing object corners. Experiments have been performed using the proposed detectors to these applications. Some comparisons and evaluations have also been performed. The results have demonstrated the effectiveness of the proposed detectors in terms of feature detectability and localization.     

DYLEMA: Using walking robots for landmine detection and location

Detection and removal of antipersonnel landmines is an important worldwide concern. A huge number of landmines has been deployed over the last twenty years, and demining will take several more decades, even if no more mines were deployed in future. An adequate mine-clearance rate can only be achieved by using new technologies such as improved sensors, efficient manipulators and mobile robots. This paper presents some basic ideas on the configuration of a mobile system for detecting and locating antipersonnel landmines efficiently and effectively. The paper describes the main features of the overall system, which consists of a sensor head that can detect certain landmine types, a manipulator to move the sensor head over large areas, a locating system based on a global-positioning system, a remote supervisor computer and a legged robot used as the subsystems’ carrier. The whole system has been configured to work in a semi-autonomous mode with a view also to robot mobility and energy efficiency.     

Landmine detection and classification with complex-valued hybrid neural network using scattering parameters dataset

Neural networks have been applied to landmine detection from data generated by different kinds of sensors. Real-valued neural networks have been used for detecting landmines from scattering parameters measured by ground penetrating radar (GPR) after disregarding phase information. This paper presents results using complex-valued neural networks, capable of phase-sensitive detection followed by classification. A two-layer hybrid neural network structure incorporating both supervised and unsupervised learning is proposed to detect and then classify the types of landmines. Tests are also reported on a benchmark data.     

Two-dimensional multi-pixel anisotropic Gaussian filter for edge-line segment (ELS) detection

In order to further improve the performance of the existing anisotropic Gaussian filters and more fully take advantage of structural information of a boundary, we heuristically develop a new multi-pixel anisotropic Gaussian filter to detect edges or edge-line segments directly from low signal-to-noise ratio images. To significantly increase computational efficiency, the classical isotropic Gaussian filters are first used for quickly estimating an approximate direction along an edge; then our filter is applied to more accurately search edge-line segment direction by a few directional filter masks only near such approximate direction. By comparing the proposed filter with the isotropic Gaussian filters, we analyze two improvement factors associated with the localization and SNR of the proposed filter. Experimental results show that the proposed detector can achieve better performance than several existing edge-detection methods in the sense of noise reduction, good localization, and high edge continuity.     

Quadratic filter for the enhancement of edges in retinal images for the efficient detection and localization of diabetic retinopathy

This paper describes the design and implementation of efficient edge detection quadratic filters for better localization of microaneurysms, caused by diabetic retinopathy, in fundus retinal images. The method is based on Volterra filter that accounts for majority of polynomial nonlinearities in images. Teager filters are designed and implemented for detecting edges in retinal images generated by a fundus camera. Better localization of microaneurysms is achieved with an isotropic quadratic filter whose kernel is designed based on optimization. The noise performance of the edge detectors is tested with Gaussian and impulsive noise.     

双线性检测滤波器及其故障可检测条件

研究了双线性检测滤波器的故障可检测条件. 设计了一种检测滤波器, 并得到了其故障完全可检测的严格条件. 结果表明要使检测滤波器故障完全可检测, 系统独立的传感器和独立的状态数至少和系统故障种类相同. 这个结果符合线性空间的性质. 最后利用所构造的滤波器分析了算例.     

A Back-propagation Neural Network Landmine Detector Using the Delta-technique and S-statistic

Landmines are a major problem facing the world today; there are millions of these deadly weapons still buried in various countries around the world. Humanitarian organizations dedicate an immeasurable amount of time, effort, and money to find and remove as many of these mines as possible. Unfortunately, landmines can be made out of common materials which make the correct detection of them very difficult. This paper analyzes the effectiveness of combining certain statistical techniques with a neural network to improve detection. The detection method must not only detect the majority of landmines in the ground, it must also filter out as many of the false alarms as possible. This is the true challenge to developing landmine detection algorithms. Our approach combines a Back-Propagation Neural Network (BPNN) with statistical techniques and compares the performance of mine detection against the performance of the energy detector and the δ-technique. Our results show that the combination of the δ-technique and the S-statistics with a neural network improves the performance.     

A comparison of decision-level sensor-fusion methods for anti-personnel landmine detection

We present the sensor-fusion results obtained from measurements within the European research project ground explosive ordinance detection (GEODE) system that strives for the realisation of a vehicle-mounted, multi-sensor, anti-personnel landmine-detection system for humanitarian de-mining. The system has three sensor types: a metal detector (MD), an infrared camera (IR), and a ground penetrating radar (GPR). The output of the sensors is processed to produce confidence levels on a grid covering the test-bed. A confidence level expresses a confidence or belief in a landmine detection on a certain position. The grid with confidence levels is the input for the decision-level sensor-fusion and provides a co-registration of the sensors. The applied fusion methods are naive Bayes' approaches, Dempster–Shafer theory, fuzzy probabilities, a rule-based method, and voting techniques. To compare fusion methods and to analyse the capacity of a method to separate landmines from the background on the basis of the output of different sensors, we provide an analysis of the different methods by viewing them as discriminant functions in the sensor confidence space. The results of experiments on real sensor data are evaluated with the leave-one-out method.     

一种小波滤波器的构造与多尺度边缘检测

Marr曾经指出,人的视觉对于影像的描述具有多尺度的特性。论文基于这一思想,从二维平滑函数出发,并根据尺度呈级数变化的特点,导出了一组多尺度小波滤波器。该滤波器低通响应关于原点对称,高通响应关于原点反对称,而且截断误差很小,具有近似的紧支撑性和平滑性。同时给出了9组滤波器响应系数,可以实现由粗到精的特征提取,为影像的多尺度边缘检测提供了有用的工具。另一方面,影像经过该滤波器处理后,利用小波变换系数模的局部极大值来提取图像的边缘特征,不仅能够有效地抑制噪声,而且能够以子象素精度确定边缘的位置。最后,利用该滤波器对建筑物遥感影像进行了边缘检测试验,获得了良好的结果,为遥感影像建筑物边缘的自动提取打下了基础。     

Detection of metallic and nonmetallic concealed targets based on millimeter‐wave inverse scattering approach

A millimeter‐wave radar based on active invers scattering approach for two dimensional screening of metallic and nonmetallic concealed targets is presented. The perceived challenges of detecting a nonmetallic target exhibiting poor dynamic range for measurement systems are analyzed and discussed by comparing the performance of three different antenna sensors. A short time duration pulse with frequency sweep covering 27 to 33 GHz band is used to feed the antenna sensors. In our experimental test, we buried a concealed target consists of metallic or dielectric strips under a dielectric layer that simulates the human body model. Waveguide and printed antipodal Vivaldi antennas are considered to study the target detectability and the quality of the measured millimeter‐wave images. The use of proposed AVA resulted in a better‐quality image with lower noise effect for both metallic and nonmetallic cases.     

Covariance-guided landmine detection and discrimination using ground-penetrating radar data

Ground penetrating Radar (GPR) can detect and deliver the response signal from any buried kind of object like plastic or metallic landmines, stones, and wood sticks. It delivers three kinds of data: Ascan, Bscan, and Cscan. However, it cannot discriminate between landmines and inoffensive objects or ‘clutter.’ One-class classification is an alternative to detect landmines, especially, as landmines features data are unbalanced. In this article, we investigate the effectiveness of the Covariance-guided One-Class Support Vector Machine (COSVM) to detect, discriminate, and locate landmines efficiently. In fact, compared to existing one-class classifiers, the COSVM has the advantage of emphasizing low variance directions. Moreover, we will compare the one-class classification to multiclass classification to tease out the advantage of the former over the latter as data are unbalanced. Our method consists of extracting Ascan GPR data. Extracted features are used as an input for COSVM to discriminate between landmines and clutter. We provide an extensive evaluation of our detection method compared to other methods based on relevant state of the art one-class and multiclass classifiers, on the well-known MACADAM database. Our experimental results show clearly the superiority of using COSVM in landmine detection and localization.     

The multiscale directional bilateral filter and its application to multisensor image fusion

In this paper, a novel multiscale geometrical analysis called the multiscale directional bilateral filter (MDBF) which introduces the nonsubsampled directional filter bank into the multiscale bilateral filter is proposed. Through combining the characteristic of preserving edge of the bilateral filter with the ability of capturing directional information of the directional filter bank, the MDBF can better represent the intrinsic geometrical structure of images. The MDBF, which is a multiscale, multidirectional and shift-invariant image decomposition scheme, is used to fuse multisensor images in this paper. The source images are first decomposed into the directional detail subbands and the approximation subbands via the MDBF. Then, the directional detail subbands and the approximation subbands are fused according to the given fusion rule, respectively. Finally, the inverse MDBF is applied to the fused subbands to obtain the fused image. Experimental results over visible and infrared images and medical images demonstrate the superiority of our method compared with conventional methods in terms of visual inspection and objective measures.     

红外背景抑制与小目标检测算法

目的 针对Robinson guard滤波器的局限性和红外图像背景抑制问题,提出一种新的红外背景抑制滤波算法。方法 首先通过形态学Tophat算子对图像背景进行抑制,然后对背景抑制后的图像采用改进的Robinson guard滤波器进一步凸显目标,并通过阈值化分割出感兴趣区域,在此基础上,利用Unger平滑去除小的噪声点,最后用局部信杂比（SCR）和移动式管道滤波剔除伪目标,实现运动小目标的准确定位。结果 采用3组不同的红外背景图像序列进行实验,所提算法对不同背景均有很好的抑制效果,与传统Robinson guard滤波方法相比,本文算法不仅能更有效地保留目标的特征信息,而且对3组图像序列的小目标的检测率分别提高了1.1%、2%、11%,虚警率分别降低了14%、12%、16%。结论 本文算法能有效地检测出小目标,具备较高的准确性,对于低信噪比的图像具有良好的适应性。同时,本文算法具有较高的实时处理能力,有利于实现实时性技术应用。     

Estimation of landmine characteristics in sandy desert using neural networks

Many places in the world are heavily contaminated with landmines, which cause that many resources are not utilized. This makes landmine detection and removal challenges for research. To guarantee reliable landmine sensing system, deep analysis and many test cases are required. The proposed concept is based on application of 1 kPa external constant pressure (lower than the landmine activation pressure) to the sand surface. The resultant contact pressure distribution is dependent on the imbedded object characteristics (type and depth). Then neural networks (NN) are trained to find the inverse solution of the sand–landmine problem. In other words, when the contact pressure is known, NN can estimate the imbedded object type and depth. In this work, using finite element modeling, the existence of landmines in sand is modeled and analyzed. The resultant contact pressure distribution for five objects (1—anti-tank, 2—anti-personnel, 3—can with diameter and height of 200 mm, 4—spherical rock with 200 mm diameter, and 5—sand without any object) in sand at different depths is used in training NN. Three NN are developed to estimate the landmine characteristics. The first one is perceptron type which classifies the introduced objects in sand. The other two feed-forward NN (FFNN) are developed to estimate the depth of two landmine types. The NN detection rates of anti-tank and anti-personnel landmines are 100 and 67 % in training, and 95 and 70 % in validation, respectively. As test cases, the detection rates of the NN in case of landmine inclination angles (0°–30°) are studied. The results show same detection rates as those at no inclination. A random noise 10 % of the average signal does not affect NN detection rates, which are the same as 95 and 70 % as in validation for anti-tank and anti-personnel, respectively, while with 20 % noise detection rates are decreases to 90 and 50 % for anti-tank and anti-personnel, respectively.

     

Robust landmine detection from thermal image time series using Hough transform and rotationally invariant features

ABSTRACT

Automated detection of buried anti-personnel landmines using remote sensing techniques is very important for clearing minefields without putting lives in danger. Although thermal infrared imaging is promising, it is far from applicable to the real world in its current state-of-the-art. The most serious problem is that experiments are generally held using sandboxes or levelled and cleared soil, but real fields are, at least partially, covered with plants. In this study, we present an algorithm for landmine detection that is robust enough to detect beyond the clutter caused by partial plant cover. The first part is a hypothesis generator based on circular Hough Transform applied to images that are filtered to enhance circular structures. The second part tests the candidate landmine coordinates using rotationally invariant features, including modified Histogram of Oriented Gaussians (HOG), over multiple images taken at different times after Wiener filtering to maximize signal-to-clutter ratio. The performances of various features and classifiers are compared. The overall performance of the algorithm is demonstrated on a dataset of real-world landmine images contaminated by simulated plants. Satisfactory results are obtained up to 40% equivalent plant coverage where more than 65% of the pixels are fully or partially covered by plants.     

Edge detection by scale multiplication in wavelet domain

This paper proposes a wavelet based edge detection scheme by scale multiplication. The dyadic wavelet transforms at two adjacent scales are multiplied as a product function to magnify the edge structures and suppress the noise. Unlike many multiscale techniques that first form the edge maps at several scales and then synthesize them together, we determined the edges as the local maxima directly in the scale product after an efficient thrsholding. It is shown that the scale multiplication achieves better results than either of the two scales, especially on the localization performance. The dislocation of neighboring edges is also improved when the width of detection filter is set large to smooth noise. Experiments on natural images are compared with the Laplacian of Gaussian and Canny edge detection algorithms.