Temporally stable feature clusters for maritime object tracking in visible and thermal imagery

This paper describes a new approach to detect and track maritime objects in real time. The approach particularly addresses the highly dynamic maritime environment, panning cameras, target scale changes, and operates on both visible and thermal imagery. Object detection is based on agglomerative clustering of temporally stable features. Object extents are first determined based on persistence of detected features and their relative separation and motion attributes. An explicit cluster merging and splitting process handles object creation and separation. Stable object clus- ters are tracked frame-to-frame. The effectiveness of the approach is demonstrated on four challenging real-world public datasets

Leveraging Metadata for Computer Vision on Unmanned Aerial Vehicles

The integration of computer vision technology into Unmanned Aerial Vehicles (UAVs) has become increasingly crucial in various aerial vision-based applications. Despite the great significant success of generic computer vision methods, a considerable performance drop is observed when applied to the UAV domain. This is due to large variations in imaging conditions, such as varying altitudes, dynamically changing viewing angles, and varying capture times resulting in vast changes in lighting conditions. Furthermore, the need for real-time algorithms and the hardware constraints pose specific problems that require special attention in the development of computer vision algorithms for UAVs. In this dissertation, we demonstrate that domain knowledge in the form of meta data is a valuable source of information and thus propose domain-aware computer vision methods by using freely accessible sensor data. The pipeline for computer vision systems on UAVs is discussed, from data mission planning, data acquisition, labeling and curation, to the construction of publicly available benchmarks and leaderboards and the establishment of a wide range of baseline algorithms. Throughout, the focus is on a holistic view of the problems and opportunities in UAV-based computer vision, and the aim is to bridge the gap between purely software-based computer vision algorithms and environmentally aware robotic platforms. The results demonstrate that incorporating meta data obtained from onboard sensors, such as GPS, barometers, and inertial measurement units, can significantly improve the robustness and interpretability of computer vision models in the UAV domain. This leads to more trustworthy models that can overcome challenges such as domain bias, altitude variance, synthetic data inefficiency, and enhance perception through environmental awareness in temporal scenarios, such as video object detection, tracking and video anomaly detection. The proposed methods and benchmarks provide a foundation for future research in this area, and the results suggest promising directions for developing environmentally aware robotic platforms. Overall, this work highlights the potential of combining computer vision and robotics to tackle real-world challenges and opens up new avenues for interdisciplinary research

DragonflEYE: a passive approach to aerial collision sensing

"This dissertation describes the design, development and test of a passive wide-field optical aircraft collision sensing instrument titled 'DragonflEYE'. Such a ""sense-and-avoid"" instrument is desired for autonomous unmanned aerial systems operating in civilian airspace. The instrument was configured as a network of smart camera nodes and implemented using commercial, off-the-shelf components. An end-to-end imaging train model was developed and important figures of merit were derived. Transfer functions arising from intermediate mediums were discussed and their impact assessed. Multiple prototypes were developed. The expected performance of the instrument was iteratively evaluated on the prototypes, beginning with modeling activities followed by laboratory tests, ground tests and flight tests. A prototype was mounted on a Bell 205 helicopter for flight tests, with a Bell 206 helicopter acting as the target. Raw imagery was recorded alongside ancillary aircraft data, and stored for the offline assessment of performance. The ""range at first detection"" (R0), is presented as a robust measure of sensor performance, based on a suitably defined signal-to-noise ratio. The analysis treats target radiance fluctuations, ground clutter, atmospheric effects, platform motion and random noise elements. Under the measurement conditions, R0 exceeded flight crew acquisition ranges. Secondary figures of merit are also discussed, including time to impact, target size and growth, and the impact of resolution on detection range. The hardware was structured to facilitate a real-time hierarchical image-processing pipeline, with selected image processing techniques introduced. In particular, the height of an observed event above the horizon compensates for angular motion of the helicopter platform.

Offshore oil seepage visible from space : a Synthetic Aperture Radar (SAR) based automatic detection, mapping and quantification system

Offshore oil seepage is believed to be the largest source of marine oil, yet very few of their locations and seepage fluxes have been discovered and reported. Natural oil seep sites are important as they serve as potential energy sources and because they are hosts to a very varied marine ecosystem. These seeps can also be associated with gas hydrates and methane emissions and hence, locating natural oil seeps can provide locations where the sources of greenhouse gases could be studied and quantified. A quantification of the amount of crude oil released from natural oil seeps is important as it can be used to set a background against which the excess anthropogenic sources of marine oil can be checked. This will provide an estimate of the 'contamination' of marine waters from anthropogenic sources. Until the onset of remote sensing techniques, field measurements and techniques like hydroacoustic measurements or piston core analysis were used to obtain knowledge about the geological settings of the seeps. The remote sensing techniques either involved manual or semi-automatic image analysis. An automatic algorithm that could quantitatively and qualitatively estimate the locations of oil seeps around the world would reduce the time and costs involved by a considerable margin. Synthetic Aperture Radar (SAR) sensors provide an illumination and weather independent source of ocean images that can be used to detect offshore oil seeps. Oil slicks on the ocean surface dampen the small wind driven waves present on the ocean surface and appear darker against the brighter ocean surface. They can, hence, be detected in SAR image. With the launch of the latest Sentinel-1 satellite aimed at providing free SAR data, an algorithm that detects oil slicks and estimates seep location is very beneficial. The global data coverage and the reduction of processing times for the large amounts of SAR data would be unmatchable. The aim of this thesis was to create such an algorithm that could automatically detect oil slicks in SAR images, map the location of the estimated oil seeps and quantify their seepage fluxes. The thesis consists of three studies that are compiled into one of more manuscripts that are published, accepted for publication or ready for submission. The first study of this thesis involves the creation of the Automatic Seep Location Estimator (ASLE) which detects oil slicks in marine SAR images and estimates offshore oil seepage sites. This, the first fully automatic oil seep location estimation algorithm, has been implemented in the programming language Python and has been tested and validated on ENVISAT images of the Black Sea. The second study reported in this thesis focuses on the optimisation of the created ASLE and comparison of the ASLE with other existing algorithms. It also describes the efficiency of the ASLE with respect to other existing algorithms and the results show that the ASLE can successfully detect seeps of active seepages. The third study aimed to provide the status of the offshore seepage in the southern Gulf of Mexico estimated from the ASLE using SAR images from ENVISAT and RADARSAT-1. The ASLE was used to detect natural oil slicks from SAR images and estimate the locations of feeding seeps. The estimated seep locations and the slicks contributing to these estimations were then analysed to quantify their seepage fluxes and rates. The three case studies illustrate that an automatic offshore seepage detection and estimation system such as the Automatic Seep Location Estimator (ASLE) is very beneficial in order to locate global oil seeps and estimate global seepage fluxes. It provides a technique to detect offshore seeps and their seepage fluxes in a fast and highly efficient manner by using Synthetic Aperture Radar images. This allows global accessibility of offshore oil seepage sites. The availability of large amounts of historic SAR datasets, the presence of 5 active SAR satellites and the latest launch of the European Space Agency satellite Sentinel-1, which provides free data, shows that there is no shortage in the availability of SAR data. The result of the work done in this thesis provides a means to utilise this large SAR dataset for the purpose of offshore oil seepage detection and offshore seepage related geophysical applications. The created system will be an important tool in the future not just to estimate offshore seepage in local seas but in global oceans that are otherwise challenging for field analysis

Fragmented Landscapes: An Archaeology of Transformations in The Pra River Basin, Southern Ghana

This doctoral archaeological research examines the Pra River Basin in southern Ghana through lenses of landscape, temporality, and transformation. Drawing on the Annales school and the writings of Tim Ingold, this study moves away from binary constructions of natural and cultural landscape features toward a more integrated view of the landscape\u27s long human history. The primary temporal focus of this research is the past three millennia but evidence recovered of even more ancient eras is also examined. The artifacts and features documented while surveying this landscape allow us to glimpse pre-Atlantic (pre-1450 CE) settlement patterns, subsistence, and technology, as well as more recent and ongoing transformations of the landscape. Artifacts including ceramics, quartz flakes, stone beads, ground stone tools, and iron slag were found on hilltop sites throughout the surveyed areas. Most of these sites represent a pre-Atlantic pattern of settlement that continues, to a lesser extent, into the early Atlantic era (1450-1700 CE). Long grinding slicks, possibly related to Nyame Akuma production, are present on numerous rock outcrops in the region. Test excavation at an iron smelting site near Adiembra (AD31) yielded a temporally extensive range of dates. The bulk of the slag was deposited in the early second century CE, but deeper ceramic bearing contexts stretched back through the first millennium BCE. A single early seventh millennium BCE date associated with stone flakes underlay the site, representing the oldest date recovered from an archaeological context in the region. The archaeological evidence this study presents suggests the entire landscape has undergone continual alteration for numerous millennia, but much of the landscape\u27s current form represents Atlantic influences and more recent historical dynamics and transformations of the colonial and post-colonial periods. I examine this fragmented landscape using satellite remote sensing, archaeological pedestrian survey, diagnostic artifact analyses, and limited test excavations to identify and assess features and transformative processes

Quantifying the spatial and temporal response of UTH and OLR to deep convection over Tropical Africa

Upper Tropospheric Humidity (UTH) has a strong control on clear-sky Outgoing Longwave Radiation (OLR). Moisture from the boundary layer is transported to the drier upper troposphere by convective ascent in the tropics and realised in the form of deep convective clouds. The spatial and temporal response of UTH and the corresponding OLR are cause for debate. This study uses geostationary satellite imagery from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) to estimate UTH using water vapour channel radiances. Deep convection over Tropical Africa is detected using the difference between 6.2 mm and 7.3 mm brightness temperatures. The sensitivity of TOA brightness temperatures to cloud properties including cloud top height and optical depth are modelled using the Santa Barbara Disort Atmospheric Radiative Transfer model with thresholds developed using colocated matchups with CloudSat and CALIPSO cloud classifications. The most appropriate thresholds are determined using probability statistics and receiver operating characteristic curves. Deep convective clouds are tracked over their lifetime in June and December 2010 using a cloud tracking algorithm, based on an area overlap method. A general robust pattern in the UTH response emerges. A stronger response of UTH is found in the spatial domain than that over the temporal domain. UTH decreases with distance from the cloud edge, whilst a small increase is seen over the cloud lifetime. This was found to be controlled by cloud size and cloud lifetime, with larger and longer lived clouds causing a stronger perturbation in UTH. The UTH response was found to be stronger in June than in December. A strong negative correlation is found between UTH and OLR perturbations, with OLR measured using the Geostationary Earth Radiation Budget (GERB) instrument. This pattern is stronger in December than June.Open Acces