Evaluación de la degradación de la tierra usando la entropía de shannon sobre imágenes polarimétricas en desiertos costeros Patagónicos

En esta investigación se focalizó en la Entropía de Shannon (ES) para la caracterización de imágenes polarimétricas de apertura sintética. Este parámetro analiza la contribución de la información por pixeles individuales para toda la imagen en la evaluación de la degradación de la tierra en imágenes ALOS PALSAR. Escenas de polarización dual y cuádruple fueron adquiridas bajo el proyecto SAOCOM (Satélite Argentino de Observación con Microondas) en 2010 y 2011, del desierto costero noreste patagónico, Argentina. Los mapas fueron verificados con información de alta verosimilitud para la misma área de estudio. Los resultados muestran que la ES puede describir y precisar las características de las imágenes de manera obvia, de tal manera que representa un valor de referencia para la detección de la degradación de la tierra y la extracción de las características de los diferentes estados y transiciones.We focus on Shannon Entropy (SE) for the characterization of polarimetric Synthetic Aperture Radar (PolSAR) images. This approach analyzes the information contribution made by individual pixels to the whole image for assessment of land degradation in the information content of ALOS PALSAR images. Additionally, the performance of other polarization parameters, and polarization decomposition is illustrated and discussed. Dual-Pol and Quad-Pol scenes have been acquired under the SAOCOM (Satélite Argentino de Observación con Microondas, Spanish for Argentine Microwaves Observation Satellite) project in 2010 and 2011, from northeastern Patagonian coastal desert, Argentina. The accuracy of the SE map was assessed using a set of ground observations based on remotely sensed data that have higher accuracy. The results show that the SE can describe and determine the image features more obviously in the study area, so that it represents an important reference value for land degradation detection and land status characteristics extraction .Fil: del Valle, Hector Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagonico; ArgentinaFil: Hardtke, Leonardo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagonico; ArgentinaFil: Blanco, Paula Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagonico; ArgentinaFil: Sione, Walter Fabian. Universidad Autónoma de Entre Ríos. Fac de Ciencia y Tecnologia. Centro Regional de Geomatica; Argentina. Universidad Nacional de Luján; Argentin

Multi-frequency and multi-attribute GPR data fusion based on 2-D wavelet transform

High frequency GPR signals offer high resolution while low frequency GPR signals offer greater depth of penetration. Effective fusion of multiple frequencies can combine the advantages of both. In addition, GPR attribute analysis can improve subsurface imaging, but a single attribute can only partly highlight details of different physical and geometrical properties of subsurface potential targets. In order to overcome these challenges, we implement an advanced multi-frequency and multi-attribute GPR data fusion approach based on 2-D wavelet transform utilizing a dynamic fusion weight scheme derived from edge detection algorithm, which is tested on data from a small glacier in the north-eastern Alps by 250 & 500 MHz central frequency antennas. Besides, information entropy and spatial frequency are developed as quantitative evaluation parameters to analyze the fusion outcomes. The results demonstrate that the proposed approach can enhance the efficiency and scope of GPR data interpretation in an automatic and objective way

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

Imperial Users onl

Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system

Cognitive GPR for subsurface sensing based on edge computing and deep reinforcement learning

Ground penetrating radars (GPRs) have been extensively used in many industrial applications, such as coal mining, structural health monitoring, subsurface utilities detection and localization, and autonomous driving. Most of the existing GPR systems are human-operated due to the need for experience in operation configurations based on the interpretation of collected GPR data. To achieve the best subsurface sensing performance, it is desired to design an autonomous GPR system that can operate adaptively under varying sensing conditions. In this research, first, a generic architecture for cognitive GPRs based on edge computing is studied. The operation of cognitive GPRs under this architecture is formulated as a sequential decision process. Then a cognitive GPR based on 2D B-Scan image analysis and deep Q-learning network (DQN) is investigated. A novel entropy-based reward function is designed for the DQN model by using the results of subsurface object detection (via the region of interest identification) and recognition (via classification). Furthermore, to acquire a global view of subsurface objects with complex shape configurations, 2D B-Scan image analysis is extended to 3D GPR data analysis termed “Scan Cloud.” A scan cloud-enabled cognitive GPR is studied based on an advanced deep reinforcement learning method called deep deterministic policy gradient (DDPG) with a new reward function derived from 3D GPR data. The proposed methods are evaluated using GPR modeling and simulation software called GprMax. Simulation results show that our proposed cognitive GPRs outperform other GPR systems in terms of detection accuracy, operating time, and object reconstruction

Determination of leakage from subsurface containment systems using informational entropy and hydraulic signature assessment methods.

The use of physical and hydraulic containment systems for the isolation of contaminated ground water and aquifer materials associated with hazardous waste sites has increased during the last decade. The existing methodologies for monitoring and evaluating leakage from hazardous waste containment systems rely primarily on limited hydraulic head data. The number of hydraulic head monitoring points available at most sites employing physical containment systems may be insufficient to identify significant leakage from the systems. A general approach for evaluating the performance of containment systems, based on relative spatial and temporal hydraulic head distributions is used to introduce two methodologies for estimating the minimum number of monitoring points necessary to identify the hydraulic signature of leakage from a containment system. Three-dimensional ground-water flow modeling results are used to illustrate the utility of a probabilistic method, based on the principles of geometric probability. Leakage from a vertical barrier containment system is simulated using a variety of hydrogeologic conditions ranging from homogeneous to heterogeneous, and isotropic to anisotropic. The second method utilizes informational entropy to quantify the spatial variability of hydraulic signatures associated with containment system leakage in the presence of background noise and trend surfaces

Applying generative adversarial networks to intelligent subsurface imaging and identification

To augment training data for machine learning models in Ground Penetrating Radar (GPR) data classification and identification, this thesis focuses on the generation of realistic GPR data using Generative Adversarial Networks. An innovative GAN ar- chitecture is proposed for generating GPR B-scans, which is, to the author’s knowledge, the first successful application of GAN to GPR B-scans. As one of the major contri- butions, a novel loss function is formulated by merging frequency domain with time domain features. To test the efficacy of generated B-scans, a real time object classifier is proposed to measure the performance gain derived from augmented B-Scan images. The numerical experiment illustrated that, based on the augmented training data, the proposed GAN architecture demonstrated a significant increase (from 82% to 98%) in the accuracy of the object classifier

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

Assessing the Viability of Complex Electrical Impedance Tomography (EIT) with a Spatially Distributed Sensor Array for Imaging of River Bed Morphology: a Proof of Concept (Study)

This report was produced as part of a NERC funded ‘Connect A’ project to establish a new collaborative partnership between the University of Worcester (UW) and Q-par Angus Ltd. The project aim was to assess the potential of using complex Electrical Impedance Tomography (EIT) to image river bed morphology. An assessment of the viability of sensors inserted vertically into the channel margins to provide real-time or near real-time monitoring of bed morphology is reported. Funding has enabled UW to carry out a literature review of the use of EIT and existing methods used for river bed surveys, and outline the requirements of potential end-users. Q-par Angus has led technical developments and assessed the viability of EIT for this purpose. EIT is one of a suite of tomographic imaging techniques and has already been used as an imaging tool for medical analysis, industrial processing and geophysical site survey work. The method uses electrodes placed on the margins or boundary of the entity being imaged, and a current is applied to some and measured on the remaining ones. Tomographic reconstruction uses algorithms to estimate the distribution of conductivity within the object and produce an image of this distribution from impedance measurements. The advantages of the use of EIT lie with the inherent simplicity, low cost and portability of the hardware, the high speed of data acquisition for real-time or near real-time monitoring, robust sensors, and the object being monitored is done so in a non-invasive manner. The need for sophisticated image reconstruction algorithms, and providing images with adequate spatial resolution are key challenges. A literature review of the use of EIT suggests that to date, despite its many other applications, to the best of our knowledge only one study has utilised EIT for river survey work (Sambuelli et al 2002). The Sambuelli (2002) study supported the notion that EIT may provide an innovative way of describing river bed morphology in a cost effective way. However this study used an invasive sensor array, and therefore the potential for using EIT in a non-invasive way in a river environment is still to be tested. A review of existing methods to monitor river bed morphology indicates that a plethora of techniques have been applied by a range of disciplines including fluvial geomorphology, ecology and engineering. However, none provide non-invasive, low costs assessments in real-time or near real-time. Therefore, EIT has the potential to meet the requirements of end users that no existing technique can accomplish. Work led by Q-par Angus Ltd. has assessed the technical requirements of the proposed approach, including probe design and deployment, sensor array parameters, data acquisition, image reconstruction and test procedure. Consequently, the success of this collaboration, literature review, identification of the proposed approach and potential applications of this technique have encouraged the authors to seek further funding to test, develop and market this approach through the development of a new environmental sensor