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

River landform dynamics detection and responses to morphology change in the rivers of North Luzon, the Philippines

River morphology detection has been improved considerably with the application of remote sensing and developments in computer science. However, applications that extract landforms within the active river channel remain limited, and there is a lack of studies from tropical regions. This thesis developed and then applied a workflow employing Sentinel-2 imagery for seasonal and annual river landform classification. Image downscaling approaches were investigated, and the performance of object-based image segmentation was assessed. The area to point regression kriging (ATPRK) approach was chosen to downscale coarser 20 m resolution Sentinel-2 bands to finer 10 m resolution bands. All features were set or processed at 10 m resolution before applying support vector machine (SVM) classification. To improve machine learning classification accuracy, Sentinel-2 acquisitions across one year, which incorporates multiple seasons, should be used. For rivers with different hydrological or geology settings, the thesis considered collecting river specific ground truth data to build a training model to avoid underfitting of models from other hydrological/geological settings. Applying the workflow, three landforms (water, unvegetated bars and vegetated bars) were classified within the active channel of the Bislak, Laoag, Abra and Cagayan Rivers, north Luzon, the Philippines, between 2016 to 2021, respectively. The spatial-temporal river landform datasets enabled the quantitative analysis of the river morphology changes. Water and unvegetated bars showed clear seasonal dynamics in all four rivers, whilst vegetated bars only showed seasonality in the rivers located in the northwest Luzon (the Bislak, Laoag and Abra Rivers). This thesis employed correlated coefficients to investigate the longitudinal correlation between river landforms and active width. It was found that vegetated bar areas always have strong significant correlations (≥0.67) with the active widths in all four rivers, whilst correlation coefficients between vegetated bar areas and active widths in the wet season are higher than that in the dry season. Ensemble empirical mode decomposition (EEMD) was applied to detect landform periodicity; this method indicated that water and vegetated bars commonly showed synchronised fluctuations with precipitation, while unvegetated bars had an anti-phase oscillation with precipitation. In the case of EEMD, deviations from periodic consistency in river pattern may reflect the influence of extreme events and/or human disturbance. Coefficient of variation (COV) was then used to evaluate the stability of the landforms; results suggested that the interplay of faults, elevation, confinement and tributary locations impacted landform stability. Finally, tributary inflow impacts on the mainstem river were investigated for eight tributaries of the lowland Cagayan River, also on Luzon Island. Longitudinal variations in channel morphology and stability, and temporal changes in landform frequency, using Simpson’s diversity index and COV, showed downstream widening associated with tributaries that was controlled by water discharge, with a secondary sediment flux effect. Overall, this thesis provided a novel example of combining remote sensing and GIS science, computing science, statistical science, and river morphology science to study the earth surface processes synthetically and quantitatively within river active channels in the tropical north Luzon, the Philippines. This work demonstrated how the fusion of techniques from these disciplines can be used to detect and analyse river landform changes, with potential applications for river management and restoration

Realistic reconstruction and rendering of detailed 3D scenarios from multiple data sources

During the last years, we have witnessed significant improvements in digital terrain modeling, mainly through photogrammetric techniques based on satellite and aerial photography, as well as laser scanning. These techniques allow the creation of Digital Elevation Models (DEM) and Digital Surface Models (DSM) that can be streamed over the network and explored through virtual globe applications like Google Earth or NASA WorldWind. The resolution of these 3D scenes has improved noticeably in the last years, reaching in some urban areas resolutions up to 1m or less for DEM and buildings, and less than 10 cm per pixel in the associated aerial imagery. However, in rural, forest or mountainous areas, the typical resolution for elevation datasets ranges between 5 and 30 meters, and typical resolution of corresponding aerial photographs ranges between 25 cm to 1 m. This current level of detail is only sufficient for aerial points of view, but as the viewpoint approaches the surface the terrain loses its realistic appearance. One approach to augment the detail on top of currently available datasets is adding synthetic details in a plausible manner, i.e. including elements that match the features perceived in the aerial view. By combining the real dataset with the instancing of models on the terrain and other procedural detail techniques, the effective resolution can potentially become arbitrary. There are several applications that do not need an exact reproduction of the real elements but would greatly benefit from plausibly enhanced terrain models: videogames and entertainment applications, visual impact assessment (e.g. how a new ski resort would look), virtual tourism, simulations, etc. In this thesis we propose new methods and tools to help the reconstruction and synthesis of high-resolution terrain scenes from currently available data sources, in order to achieve realistically looking ground-level views. In particular, we decided to focus on rural scenarios, mountains and forest areas. Our main goal is the combination of plausible synthetic elements and procedural detail with publicly available real data to create detailed 3D scenes from existing locations. Our research has focused on the following contributions: - An efficient pipeline for aerial imagery segmentation - Plausible terrain enhancement from high-resolution examples - Super-resolution of DEM by transferring details from the aerial photograph - Synthesis of arbitrary tree picture variations from a reduced set of photographs - Reconstruction of 3D tree models from a single image - A compact and efficient tree representation for real-time rendering of forest landscapesDurant els darrers anys, hem presenciat avenços significatius en el modelat digital de terrenys, principalment gràcies a tècniques fotogramètriques, basades en fotografia aèria o satèl·lit, i a escàners làser. Aquestes tècniques permeten crear Models Digitals d'Elevacions (DEM) i Models Digitals de Superfícies (DSM) que es poden retransmetre per la xarxa i ser explorats mitjançant aplicacions de globus virtuals com ara Google Earth o NASA WorldWind. La resolució d'aquestes escenes 3D ha millorat considerablement durant els darrers anys, arribant a algunes àrees urbanes a resolucions d'un metre o menys per al DEM i edificis, i fins a menys de 10 cm per píxel a les fotografies aèries associades. No obstant, en entorns rurals, boscos i zones muntanyoses, la resolució típica per a dades d'elevació es troba entre 5 i 30 metres, i per a les corresponents fotografies aèries varia entre 25 cm i 1m. Aquest nivell de detall només és suficient per a punts de vista aeris, però a mesura que ens apropem a la superfície el terreny perd tot el realisme. Una manera d'augmentar el detall dels conjunts de dades actuals és afegint a l'escena detalls sintètics de manera plausible, és a dir, incloure elements que encaixin amb les característiques que es perceben a la vista aèria. Així, combinant les dades reals amb instàncies de models sobre el terreny i altres tècniques de detall procedural, la resolució efectiva del model pot arribar a ser arbitrària. Hi ha diverses aplicacions per a les quals no cal una reproducció exacta dels elements reals, però que es beneficiarien de models de terreny augmentats de manera plausible: videojocs i aplicacions d'entreteniment, avaluació de l'impacte visual (per exemple, com es veuria una nova estació d'esquí), turisme virtual, simulacions, etc. En aquesta tesi, proposem nous mètodes i eines per ajudar a la reconstrucció i síntesi de terrenys en alta resolució partint de conjunts de dades disponibles públicament, per tal d'aconseguir vistes a nivell de terra realistes. En particular, hem decidit centrar-nos en escenes rurals, muntanyes i àrees boscoses. El nostre principal objectiu és la combinació d'elements sintètics plausibles i detall procedural amb dades reals disponibles públicament per tal de generar escenes 3D d'ubicacions existents. La nostra recerca s'ha centrat en les següents contribucions: - Un pipeline eficient per a segmentació d'imatges aèries - Millora plausible de models de terreny a partir d'exemples d’alta resolució - Super-resolució de models d'elevacions transferint-hi detalls de la fotografia aèria - Síntesis d'un nombre arbitrari de variacions d’imatges d’arbres a partir d'un conjunt reduït de fotografies - Reconstrucció de models 3D d'arbres a partir d'una única fotografia - Una representació compacta i eficient d'arbres per a navegació en temps real d'escenesPostprint (published version

Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree

13 p.Safe roads are a necessity for any society because of the high social costs of traffic accidents. This challenge is addressed by a novel methodology that allows us to evaluate road safety from Mobile LiDAR System data, taking advantage of the road alignment due to its influence on the accident rate. Automation is obtained through an inductive reasoning process based on a decision tree that provides a potential risk assessment. To achieve this, a 3D point cloud is classified by an iterative and incremental algorithm based on a 2.5D and 3D Delaunay triangulation, which apply different algorithms sequentially. Next, an automatic extraction process of road horizontal alignment parameters is developed to obtain geometric consistency indexes, based on a joint triple stability criterion. Likewise, this work aims to provide a powerful and effective preventive and/or predictive tool for road safety inspections. The proposed methodology was implemented on three stretches of Spanish roads, each with different traffic conditions that represent the most common road types. The developed methodology was successfully validated through as-built road projects, which were considered as “ground truth.”S

A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

Spatiotemporal Data Augmentation of MODIS-LANDSAT Water Bodies Using Generative Adversarial Networks

The monitoring of the shape and area of a water body is an essential component for many Earth science and Hydrological applications. For this purpose, these applications require remote sensing data which provides accurate analysis of the water bodies. In this thesis the same is being attempted, first, a model is created that can map the information from one kind of satellite that captures the data from a distance of 500m to another data that is captured by a different satellite at a distance of 30m. To achieve this, we first collected the data from both of the satellites and translated the data from one satellite to another using our proposed Hydro-GAN model. This translation gives us the accurate shape, boundary, and area of the water body. We evaluated the method by using several different similarity metrics for the area and the shape of the water body. The second part of this thesis involves augmenting the data that we obtained from the Hydro-GAN model with the original data and using this enriched data to predict the area of a water body in the future. We used the case study of Great Salt lake for this purpose. The results indicated that our proposed model was creating accurate area and shape of the water bodies. When we used our proposed model to generate data at a resolution of 30m it gave us better areal and shape accuracy. If we get more data at this resolution, we can use that data to better predict coastal lines, boundaries, as well as erosion monitoring

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Building Detection from Very High Resolution Remotely Sensed Imagery Using Deep Neural Networks

The past decades have witnessed a significant change in human societies with a fast pace and rapid urbanization. The boom of urbanization is contributed by the influx of people to the urban area and comes with building construction and deconstruction. The estimation of both residential and industrial buildings is important to reveal and demonstrate the human activities of the regions. As a result, it is essential to effectively and accurately detect the buildings in urban areas for urban planning and population monitoring. The automatic building detection method in remote sensing has always been a challenging task, because small targets cannot be identified in images with low resolution, as well as the complexity in the various scales, structure, and colours of urban buildings. However, the development of techniques improves the performance of the building detection task, by taking advantage of the accessibility of very high-resolution (VHR) remotely sensed images and the innovation of object detection methods. The purpose of this study is to develop a framework for the automatic detection of urban buildings from the VHR remotely sensed imagery at a large scale by using the state-of-art deep learning network. The thesis addresses the research gaps and difficulties as well as the achievements in building detection. The conventional hand-crafted methods, machine learning methods, and deep learning methods are reviewed and discussed. The proposed method employs a deep convolutional neural network (CNN) for building detection. Two input datasets with different spatial resolutions were used to train and validate the CNN model, and a testing dataset was used to evaluate the performance of the proposed building detection method. The experiment result indicates that the proposed method performs well at both building detection and outline segmentation task with a total precision of 0.92, a recall of 0.866, an F1-score of 0.891. In conclusion, this study proves the feasibility of CNN on solving building detection challenges using VHR remotely sensed imagery