Buried landmines and unexploded remnants of war are a constant threat for the population of many countries that have been hit by wars in the past years. The huge amount of casualties has been a strong motivation for the research community toward the development of safe and robust techniques designed for landmine clearance. Nonetheless, being able to detect and localize buried landmines with high precision in an automatic fashion is still considered a challenging task due to the many different boundary conditions that characterize this problem (e.g., several kinds of objects to detect, different soils and meteorological conditions, etc.). In this article, we propose a novel technique for buried object detection tailored to unexploded landmine discovery. The proposed solution exploits a specific kind of convolutional neural network (CNN) known as autoencoder to analyze volumetric data acquired with ground penetrating radar (GPR) using different polarizations. This method works in an anomaly detection framework, indeed we only train the autoencoder on GPR data acquired on landmine-free areas. The system then recognizes landmines as objects that are dissimilar to the soil used during the training step. Experiments conducted on real data show that the proposed technique requires little training and no ad hoc data preprocessing to achieve accuracy higher than 93% on challenging data sets.

The United Nations 2030 Agenda for Sustainable Development highlighted the importance of adopting sustainable agricultural practices to mitigate the threat posed by climate change to food systems around the world, to provide wise water management and to restore degraded lands. At the same time, it suggested the benefits and advantages brought by the use of near-surface geophysical measurements to assist precision farming, in particular providing information on soil variability at both vertical and horizontal scales. Among such survey methodologies, Ground Penetrating Radar has demonstrated its effectiveness in soil characterisation as a consequence of its sensitivity to variations in soil electrical properties and of its additional capability of investigating subsurface stratification. The aim of this contribution is to provide a comprehensive review of the current use of the GPR technique within the domain of precision irrigation, and specifically of its capacity to provide detailed information on the within-field spatial variability of the textural, structural and hydrological soil properties, which are needed to optimize irrigation management, adopting a variable-rate approach to preserve water resources while maintaining or improving crop yields and their quality. For each soil property, the review analyses the commonly adopted operational and data processing approaches, highlighting advantages and limitations.

This paper focuses on the application of ground penetrating radar (GPR) technique for civil engineering purposes, addressing the issues related to wave polarisation and antennas geometry. Even if polarisation of GPR signal is often an underestimated feature during data analysis and post processing, detection or avoidance of a specific target can be managed handling its polarimetric response. This opportunity is of high importance in this field of application, where the mixture of target with different polarimetric response is a commonly encountered situation. To provide an insight of this, two multicomponent GPR surveys have been performed: a first survey to show the effect of antenna-target mutual alignment variation and a second experiment in which the benefits of acquiring with different antenna arrangements are clearly evident. Because each antenna arrangement is sensitive towards different features of the received wavefield, this strategy is able to discriminate targets depending on their geometrical shape, thus delivering better detailed image of the acquired area.

Soil morphology plays a fundamental role in the vertical and lateral movements of solutes and water transport, providing knowledge regarding spatial distribution of its textural properties and subsurface dynamics. In this framework, the measured values of electrical conductivity are able to reveal the heterogeneity of soil that is present in a particular agricultural field and they are affected by more than one important physical characteristic: soil texture, organic matter, moisture content, and the depth of the clay pan. In the microwave region, these dynamics are known to exhibit a frequency dependent behaviour. This study explores the application of a Step Frequency Continuous Wave Ground Penetrating Radar (SFCW GPR) to shed light on the practical impact that these dependencies have on the imaging results, not only regarding the electrical characterisation of the subsurface morphology, but also in its correct interpretation. This information is of notable importance for determining water-use efficiency and planning precision-agriculture programs. The results clearly show visible and significant fluctuations of the amplitude levels, depending on the considered central frequency, demonstrating that the frequency dependence of electromagnetic properties of heterogeneous soil are significant and cannot be ignored if the aim is to properly define the subsurface attributes. The measurements also suggest that correlating the delineated variations might help in the identification of extended features and the classification of areas that possess similar properties in order to increase the confidence in monitoring soil resources.