Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Evaluation and improvement of methods for estimating sea surface wave parameters from X-band marine radar data

In this thesis, several algorithms have been proposed for estimating ocean wave parameters from X-band marine radar data, i.e., wave direction, wave period, and significant wave height. In the first part of this study, the accuracy of wave direction and period estimation from X-band marine radar images under different rain rates is analyzed, and a sub-image selection scheme is proposed to mitigate the rain effect. Firstly, each radar image is divided into multiple sub-images, and the sub-images with relatively clear wave signatures are identified based on a random-forest based classiffication model. Then, wave direction is estimated by performing a Radon transform (RT) on each valid sub-image. As for wave period estimation, a random-forest based regression method is proposed. Texture features are first extracted from each pixel of the selected sub-image using the gray-level co-occurrence matrix (GLCM) and combined as a feature vector. Those feature vectors extracted from both rain-free and rain-contaminated training samples are then used to train a random-forest based wave period regression model. Shore-based X-band marine radar images, simultaneous rain rate data, as well as buoy-measured wave data collected on the West Coast of the United States are used to analyze the rain effect on wave parameter estimation accuracy and to validate the proposed method. Experimental results show that the proposed subimage selection scheme improves the estimation accuracy of both wave direction and wave period under different rain rates, with reductions of root-mean-square errors (RMSEs) by 6.9ﾟ, 6.0ﾟ, 4.9ﾟ, and 1.0ﾟ for wave direction under rainless, light rain, moderate rain, and heavy rain conditions, respectively. As for wave period estimation, the RMSEs decrease by 0.13 s, 0.20 s, 0.30 s, and 0.20 s under those four rainfall intensity levels, respectively. The second part of research focuses on the estimation of significant wave height (Hₛ). A temporal convolutional network (TCN)-based model is proposed to retrieve Hₛ from X-band marine radar image sequences. Three types of features are first extracted from radar image sequences based on signal to noise ratio (SNR), ensemble empirical mode decomposition (EEMD), and GLCM methods, respectively. Then, feature vectors are input into the proposed TCN-based regression model to produce Hₛ estimation. Radar data are collected from a moving vessel at the East Coast of Canada, as well as simultaneously collected wave data from several wave buoys deployed nearby are used for model training and testing. After averaging, experimental results show that the TCN-based model further improves the Hₛ estimation accuracy, with reductions of RMSEs by 0.33 m and 0.10 m, respectively, compared to the SNR-based and the EEMD-based linear fitting methods. It has also been found that with the same feature extraction scheme, TCN outperforms other machine-learning based algorithms including support vector regression (SVR) and the convolutional gated recurrent unit (CGRU) network

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Sea surface wind and wave parameter estimation from X-band marine radar images with rain detection and mitigation

In this research, the application of X-band marine radar backscatter images for sea surface wind and wave parameter estimation with rain detection and mitigation is investigated. In the presence of rain, the rain echoes in the radar image blur the wave signatures and negatively affect estimation accuracy. Hence, in order to improve estimation accuracy, it is meaningful to detect the presence of those rain echoes and mitigate their influence on estimation results. Since rain alters radar backscatter intensity distribution, features are extracted from the normalized histogram of each radar image. Then, a support vector machine (SVM)-based rain detection model is proposed to classify radar images obtained between rainless and rainy conditions. The classification accuracy shows significant improvement compared to the existing threshold-based method. By further observing images obtained under rainy conditions, it is found that many of them are only partially contaminated by rain echoes. Therefore, in order to segment between rain-contaminated regions and those that are less or unaffected by rain, two types of methods are developed based on unsupervised learning techniques and convolutional neural network (CNN), respectively. Specifically, for the unsupervised learning-based method, texture features are first extracted from each pixel and then trained using a self organizing map (SOM)-based clustering model, which is able to conduct pixel-based identification of rain-contaminated regions. As for the CNN-based method, a SegNet-based semantic segmentation CNN is �rst designed and then trained using images with manually annotated labels. Both shipborne and shore-based marine radar data are used to train and validate the proposed methods and high classification accuracies of around 90% are obtained. Due to the similarities between how haze affects terrestrial images and how rain affects marine radar images, a type of CNN for image dehazing purposes, i.e., DehazeNet, is applied to rain-contaminated regions in radar images for correcting the in uence of rain, which reduces the estimation error of wind direction significantly. Besides, after extracting histogram and texture features from rain-corrected radar images, a support vector regression (SVR)-based model, which achieves high estimation accuracy, is trained for wind speed estimation. Finally, a convolutional gated recurrent unit (CGRU) network is designed and trained for significant wave height (SWH) estimation. As an end-to-end system, the proposed network is able to generate estimation results directly from radar image sequences by extracting multi-scale spatial and temporal features in radar image sequences automatically. Compared to the classic signal-to-noise (SNR)-based method, the CGRU-based model shows significant improvement in both estimation accuracy (under both rainless and rainy conditions) and computational efficiency

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Study on Real-Time Ocean Wave Analysis Based on X-Band Radar Measurement Data

학위논문(석사) -- 서울대학교대학원 : 공과대학 조선해양공학과, 2023. 2. 김용환.해양 활동의 안전성 및 효율을 향상하기 위해 신뢰도 높은 파랑 정보의 획득이 요구됨에 따라 전 세계적으로 다양한 방식의 파랑 계측이 수행되고 있다. 이 중 해양 X-band 레이더는 넓은 영역의 파랑 정보를 동시에 계측할 수 있으므로 단시간의 계측을 통해 통계적으로 수렴도 높은 해양파 정보를 얻을 수 있다는 장점이 있다. 때문에, 다양한 선박 및 해양 구조물에서 해양 X-band 레이더를 활용하여 파랑 계측을 수행하고 있으며, 계측 기법의 고도화에 대한 다양한 논의가 진행되고 있다. 해양 레이더는 안테나에서 송신된 X-band 마이크로파와 해면상 잔물결 간의 Bragg 공진 현상에 의해 후방 산란되는 전자기파의 세기를 계측한다. 이러한 원격 계측 과정은 그림자, 기울임, 유체동역학적 효과 등 수많은 비물리적 변조 효과를 수반한다. 따라서, 레이더 이미지로부터 파랑 정보를 도출하기 위해서는 이미지 강도에 포함된 비물리적 성분을 제거하고 스펙트럼의 에너지를 유의파고에 따라 조정하는 파랑장 재구성 과정이 요구된다. 본 논문은 고도화된 파랑장 재구성 기법에 대한 연구를 다루고 있다. 제시된 전체 재구성 절차는 그림자 기반 유의파고 추정과 3D-FFT 기반 파랑장 재구성으로 구성되며, 각 해석 과정이 높은 연산 효율을 지니고 있다. 또한, 본 연구에서는 파랑장 재구성 기법의 정확도 향상을 위해 그림자 발생의 공간 통계적 특성을 엄밀하게 고려하였다. 이를 위해, 유의파고 추정 시 해면의 공간상 자기상관함수 및 평균표면경사의 직교성을 고려하였고, 파랑장 재구성 시 그림자 발생의 공간적 특성에 기인하는 불균일한 분산 분포에 대한 보정을 수행하였다. 본 논문에서 제시된 기법의 검증을 위해 합성 레이더 이미지와 실해역 레이더 이미지에 대해 파랑장 재구성을 수행하였다. 먼저, 다양한 해상 상태의 합성 레이더 이미지를 생성하여 해석에 활용하였고, 해상 상태에 따른 재구성 정확도의 의존성을 살펴보았다. 이를 통해, 다양한 해상 상태에서 그림자 효과에 대한 엄밀한 고려를 통해 파랑장 재구성 정확도를 향상할 수 있음을 확인하였다. 다음으로, 이어도 해양과학기지 및 기상 1호에서 계측된 실해역 레이더 이미지에 대한 유의파고 추정을 수행하였다. 그 결과, 실해역 데이터에 대하여 정확도 높은 유의파고 추정이 가능함을 확인하였다.It is required to obtain reliable wave information to improve the safety and efficiency of marine activities. Various methods for wave measurements are being carried out around the world. Among them, the marine X-band radar has the advantage that it can obtain statistically converged wave information based on short-time measurement. This is because the wave radar can simultaneously measure wave elevation data in a large area. Accordingly, marine X-band radars are installed on various ships and marine platforms to perform wave measurements. Diverse discussions on X-band radar-based wave field analysis techniques are also steadily underway. In general, incoherent marine radar measures the backscattered intensity due to Bragg scattering between X-band microwaves transmitted from the antenna and ripples on the sea surface. This remote sensing process entails numerous non-physical modulation effects, such as shadowing, tilting, and hydrodynamic effects. Therefore, a series of post-processing called wave-field reconstruction is required to retrieve wave information from marine radar images. The wave-field reconstruction procedure consists of removing the non-physical components from the measured spectrum, and adjusting the total spectral energy according to the significant wave height (HS). In this study, the advanced wave-field reconstruction technique is presented. The overall reconstruction procedure is comprised of the shadowing-based HS estimation and 3D-FFT-based wave-field reconstruction, and both of each analysis process have high computational efficiency. Thats why it is suitable for real-time wave-field analysis. To enhance the wave analysis, the statistical characteristics of the shadowing effect were rigorously considered. For this purpose, the spatial autocorrelation function of the ocean surface and the orthogonality of the mean surface slope were considered for HS estimation. Moreover, the uneven variance distribution owing to the spatial dependency of the shadowing effect was mitigated during the wave-field reconstruction. Wave-field reconstruction was applied to the synthetic and real radar images to verify the presented technique. The HS estimation and 3D-FFT-based wave-field reconstruction were performed for synthetic radar images corresponding to various states, and the dependence of this technique on the sea state was examined. As a result, it was confirmed that the reconstruction accuracy could be improved through the rigorous consideration of stochastic characteristics of the shadowing effect for all cases. Moreover, HS estimation was performed for real radar images collected from the Ieodo ocean research station and RV Gisang 1. In conclusion, a satisfactorily accurate HS estimation was also achieved.1. 서론 1 1.1 연구 배경 1 1.2 기존 연구 3 1.2.1 3D-FFT 기반 파랑장 재구성 3 1.2.2 유의파고 추정 5 1.3 연구 목표 및 주요 연구 내용 7 2. 파랑장 재구성 9 2.1 위상 분해 파랑장 재구성 문제 9 2.1.1 문제 정의 9 2.1.2 전체 해석 절차 11 2.2 유의파고 추정 13 2.2.1 그림자 영역 구분 13 2.2.2 Smith 함수 기반 표면 경사 추정 14 2.2.3 총표면경사 추정 18 2.2.4 유의파고 계산 19 2.3 3D-FFT 기반 파랑장 재구성 21 2.3.1 Mean-shift 변형 21 2.3.2 에너지 분포 보정 21 2.3.3 3차원 고속 푸리에 변환(3D-FFT) 23 2.3.4 필터링 23 2.3.5 변조 전달 함수(MTF) 24 3. 합성 레이더 이미지 해석 26 3.1 합성 레이더 이미지 생성 26 3.2 유의파고 추정 30 3.2.1 평균표면경사 추정 30 3.2.2 스펙트럼 해석 34 3.2.3 유의파고 추정 결과 36 3.3 파랑장 재구성 38 3.3.1 에너지 분포 보정 38 3.3.2 재구성 결과 40 4. 실해역 레이더 이미지 해석 45 4.1 데이터셋 정의 45 4.1.1 이어도 데이터셋 45 4.1.2 NIMS 데이터셋 46 4.2 유의파고 추정 결과 48 4.2.1 이어도 데이터셋 해석 결과 48 4.2.2 NIMS 데이터셋 해석 결과 53 5. 결론 57 6. 공학적 기여 및 향후 연구 59 참고문헌 61 부록 66 A.1 Smith 함수 유도 66 A.2 해면 평균표면경사의 물리적 특성 71석

Artificial Neural Networks and Evolutionary Computation in Remote Sensing

Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification

Artificial intelligence methods for security and cyber security systems

This research is in threat analysis and countermeasures employing Artificial Intelligence (AI) methods within the civilian domain, where safety and mission-critical aspects are essential. AI has challenges of repeatable determinism and decision explanation. This research proposed methods for dense and convolutional networks that provided repeatable determinism. In dense networks, the proposed alternative method had an equal performance with more structured learnt weights. The proposed method also had earlier learning and higher accuracy in the Convolutional networks. When demonstrated in colour image classification, the accuracy improved in the first epoch to 67%, from 29% in the existing scheme. Examined in transferred learning with the Fast Sign Gradient Method (FSGM) as an analytical method to control distortion of dissimilarity, a finding was that the proposed method had more significant retention of the learnt model, with 31% accuracy instead of 9%. The research also proposed a threat analysis method with set-mappings and first principle analytical steps applied to a Symbolic AI method using an algebraic expert system with virtualized neurons. The neural expert system method demonstrated the infilling of parameters by calculating beamwidths with variations in the uncertainty of the antenna type. When combined with a proposed formula extraction method, it provides the potential for machine learning of new rules as a Neuro-Symbolic AI method. The proposed method uses extra weights allocated to neuron input value ranges as activation strengths. The method simplifies the learnt representation reducing model depth, thus with less significant dropout potential. Finally, an image classification method for emitter identification is proposed with a synthetic dataset generation method and shows the accurate identification between fourteen radar emission modes with high ambiguity between them (and achieved 99.8% accuracy). That method would be a mechanism to recognize non-threat civil radars aimed at threat alert when deviations from those civilian emitters are detected