Anomaly detection & object classification using multi-spectral LiDAR and sonar

In this thesis, we present the theory of high-dimensional signal approximation of multifrequency signals. We also present both linear and non-linear compressive sensing (CS) algorithms that generate encoded representations of time-correlated single photon counting (TCSPC) light detection and ranging (LiDAR) data, side-scan sonar (SSS) and synthetic aperture sonar (SAS). The main contributions of this thesis are summarised as follows: 1. Research is carried out studying full-waveform (FW) LiDARs, in particular, the TCSPC data, capture, storage and processing. 2. FW-LiDARs are capable of capturing large quantities of photon-counting data in real-time. However, the real-time processing of the raw LiDAR waveforms hasn’t been widely exploited. This thesis answers some of the fundamental questions: • can semantic information be extracted and encoded from raw multi-spectral FW-LiDAR signals? • can these encoded representations then be used for object segmentation and classification? 3. Research is carried out into signal approximation and compressive sensing techniques, its limitations and the application domains. 4. Research is also carried out in 3D point cloud processing, combining geometric features with material spectra (spectral-depth representation), for object segmentation and classification. 5. Extensive experiments have been carried out with publicly available datasets, e.g. the Washington RGB Image and Depth (RGB-D) dataset [108], YaleB face dataset1 [110], real-world multi-frequency aerial laser scans (ALS)2 and an underwater multifrequency (16 wavelengths) TCSPC dataset collected using custom-build targets especially for this thesis. 6. The multi-spectral measurements were made underwater on targets with different shapes and materials. A novel spectral-depth representation is presented with strong discrimination characteristics on target signatures. Several custom-made and realistically scaled exemplars with known and unknown targets have been investigated using a multi-spectral single photon counting LiDAR system. 7. In this work, we also present a new approach to peak modelling and classification for waveform enabled LiDAR systems. Not all existing approaches perform peak modelling and classification simultaneously in real-time. This was tested on both simulated waveform enabled LiDAR data and real ALS data2 . This PhD also led to an industrial secondment at Carbomap, Edinburgh, where some of the waveform modelling algorithms were implemented in C++ and CUDA for Nvidia TX1 boards for real-time performance. 1http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ 2This dataset was captured in collaboration with Carbomap Ltd. Edinburgh, UK. The data was collected during one of the trials in Austria using commercial-off-the-shelf (COTS) sensors

Review : Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy

Pavement Surface Distress Detection, Assessment, and Modeling Using Geospatial Techniques

Roadway pavement surface distress information is essential for effective pavement asset management, and subsequently, transportation agencies at all levels dedicate a large amount of time and money to routinely collect data on pavement surface distress conditions as the core of their asset management programs. These data are used by these agencies to make maintenance and repair decisions. Current methods for pavement surface distress evaluation are time-consuming and expensive. Geospatial technologies provide new methods for evaluating pavement surface distress condition that can supplement or substitute for currently-adopted evaluation methods. However, few previous studies have explored the utility of geospatial technologies for pavement surface distress evaluation. The primary scope of this research is to evaluate the potential of three geospatial techniques to improve the efficiency of pavement surface distress evaluation, including empirical analysis of high-spatial resolution natural color digital aerial photography (HiSR-DAP), empirical analysis of hyper-spatial resolution natural color digital aerial photography (HySR-DAP), and inferential geospatial modeling based on traffic volume, environmental conditions, and topographic factors. Pavement surface distress rates estimated from the aforementioned geospatial technologies are validated against distress data manually collected using standard protocols. Research results reveal that straightforward analysis of the spectral response extracted from HiSR-DAP can permit assessment of overall pavement surface conditions. In addition, HySR-DAP acquired from S-UAS can provide accurate and reliable information to characterize detailed pavement surface distress conditions. Research results also show that overall pavement surface distress condition can be effectively estimated based on the extent of geospatial data and inferential modeling techniques. In the near term, these proposed methods could be used to rapidly and cost-effectively evaluate pavement surface distress condition for roadway sections where field inspectors or survey vehicles cannot gain access. In the long term, these proposed methods are capable of being automated to routinely evaluate pavement surface distress condition and, ultimately, to provide a cost-effective, rapid, and safer alternative to currently-adopted evaluation methods with substantially reduced sampling density

UAV data modeling for geoinformation update

A dissertação visa avaliar a relevância e o desempenho dos dados obtidos por Veículos Aéreos Não Tripulados (VANT) na atualização de Geoinformação. Os dados obtidos por VANT serão utilizados quer em conjunto com outros dados – obtidos por plataformas tradicionais de deteção remota –, quer isoladamente, recorrendo à técnica de Structure from Motion (SfM), para gerar o modelo digital de superfície e os ortomosaicos de alta precisão em diferentes momentos. Para a avaliação da precisão dos dados, os modelos digitais de terreno serão comparados. Por outro lado, os dados e informação gerados permitirão atualizar Geoinformação e quantificar as mudanças ocorridas no uso e ocupação do solo. Os resultados irão alimentar a discussão crítica da ação antrópica nos aglomerados urbanos e as propostas de intervenção.The dissertation aims to assess the relevance and performance of data obtained by Unmanned Aerial Vehicles (UAVs) in updating Geoinformation. The data obtained by UAVs will be used either in conjunction with other data – obtained by traditional remote sensing platforms – or on its own, using the Structure from Motion (SfM) technique, to generate high-precision digital surface models and orthomosaics at different times. For the accuracy assessment of the data, the digital terrain models will be compared. On the other hand, the data and information generated will make it possible to update Geoinformation and quantify changes in land use and occupation. The results will feed the critical discussion of anthropic action in urban areas and intervention proposals

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Deteksi, Klasifikasi dan Model Prediksi Tutupan Lahan Embung untuk Pertanian menggunakan Support Vector Machine dan Markov Cellular Automata

Sektor pertanian merupakan sektor andalan dalam perekonomian Kabupaten Malang. Namun Kabupaten Malang telah mengalami penurunan luas panen padi yang disebabkan oleh kekeringan. Salah satu upaya Pemerintah untuk mengatasi hal tersebut ialah dengan melakukan kegiatan pembangunan embung untuk pertanian. Penggunaan teknologi remote sensing (penginderaan jauh) merupakan salah satu alat yang efektif untuk memantau fenomena perubahan yang terjadi secara terus menerus dan dalam area yang luas dalam hal ini embung. Tujuan dari penelitian ini adalah menentukan dan menganalisis penggunaan klasifikasi SVM pada citra satelit dalam hal deteksi embung, serta mengetahui model prediksi perubahan lahan embung untuk pertanian di Kabupaten Malang. Penelitian ini menggunakan Support Vector Machine (SVM) untuk mengklasifikasi jenis tutupan lahan dan model Markov Cellular Automata (Markov-CA) untuk memprediksi perubahan tutupan lahan embung untuk pertanian berdasarkan peluang perubahan lahan. Model prediksi dibangun dengan kombinasi interval waktu yaitu tahun 2004-2009 dan 2009-2015 yang kemudian diuji untuk memprediksi tutupan lahan tahun 2015 dan 2020. Penelitian ini menggunakan citra satelit PlanetScope, Landsat 7 dan 8. Penelitian ini terdiri dari empat pekerjaan utama yaitu praproses citra satelit, klasifikasi citra satelit, deteksi dan model prediksi perubahan penggunaan lahan. Hasil penelitian menunjukan penambahan jumlah area contoh pada algoritme SVM berdampak pada waktu komputasi dan akurasi klasifikasi embung, dimana jumlah area contoh yang sedikit waktu komputasi 16 detik dan akurasi 0.5641. Sedangkan jumlah area contoh yang banyak waktu komputasi 307 detik dan akurasi 07093. Model prediksi Markov-CA memiliki akurasi yang baik daripada model aktual pada kasus deteksi perubahan lahan embung untuk pertanian di Kabupaten Malang sebesar 0.3834 dan 0.3769

Automated Extraction of Road Information from Mobile Laser Scanning Data

Effective planning and management of transportation infrastructure requires adequate geospatial data. Existing geospatial data acquisition techniques based on conventional route surveys are very time consuming, labor intensive, and costly. Mobile laser scanning (MLS) technology enables a rapid collection of enormous volumes of highly dense, irregularly distributed, accurate geo-referenced point cloud data in the format of three-dimensional (3D) point clouds. Today, more and more commercial MLS systems are available for transportation applications. However, many transportation engineers have neither interest in the 3D point cloud data nor know how to transform such data into their computer-aided model (CAD) formatted geometric road information. Therefore, automated methods and software tools for rapid and accurate extraction of 2D/3D road information from the MLS data are urgently needed. This doctoral dissertation deals with the development and implementation aspects of a novel strategy for the automated extraction of road information from the MLS data. The main features of this strategy include: (1) the extraction of road surfaces from large volumes of MLS point clouds, (2) the generation of 2D geo-referenced feature (GRF) images from the road-surface data, (3) the exploration of point density and intensity of MLS data for road-marking extraction, and (4) the extension of tensor voting (TV) for curvilinear pavement crack extraction. In accordance with this strategy, a RoadModeler prototype with three computerized algorithms was developed. They are: (1) road-surface extraction, (2) road-marking extraction, and (3) pavement-crack extraction. Four main contributions of this development can be summarized as follows. Firstly, a curb-based approach to road surface extraction with assistance of the vehicle’s trajectory is proposed and implemented. The vehicle’s trajectory and the function of curbs that separate road surfaces from sidewalks are used to efficiently separate road-surface points from large volume of MLS data. The accuracy of extracted road surfaces is validated with manually selected reference points. Secondly, the extracted road enables accurate detection of road markings and cracks for transportation-related applications in road traffic safety. To further improve computational efficiency, the extracted 3D road data are converted into 2D image data, termed as a GRF image. The GRF image of the extracted road enables an automated road-marking extraction algorithm and an automated crack detection algorithm, respectively. Thirdly, the automated road-marking extraction algorithm applies a point-density-dependent, multi-thresholding segmentation to the GRF image to overcome unevenly distributed intensity caused by the scanning range, the incidence angle, and the surface characteristics of an illuminated object. The morphological operation is then implemented to deal with the presence of noise and incompleteness of the extracted road markings. Fourthly, the automated crack extraction algorithm applies an iterative tensor voting (ITV) algorithm to the GRF image for crack enhancement. The tensor voting, a perceptual organization method that is capable of extracting curvilinear structures from the noisy and corrupted background, is explored and extended into the field of crack detection. The successful development of three algorithms suggests that the RoadModeler strategy offers a solution to the automated extraction of road information from the MLS data. Recommendations are given for future research and development to be conducted to ensure that this progress goes beyond the prototype stage and towards everyday use