Extrinsic Parameter Calibration for Line Scanning Cameras on Ground Vehicles with Navigation Systems Using a Calibration Pattern

Line scanning cameras, which capture only a single line of pixels, have been increasingly used in ground based mobile or robotic platforms. In applications where it is advantageous to directly georeference the camera data to world coordinates, an accurate estimate of the camera's 6D pose is required. This paper focuses on the common case where a mobile platform is equipped with a rigidly mounted line scanning camera, whose pose is unknown, and a navigation system providing vehicle body pose estimates. We propose a novel method that estimates the camera's pose relative to the navigation system. The approach involves imaging and manually labelling a calibration pattern with distinctly identifiable points, triangulating these points from camera and navigation system data and reprojecting them in order to compute a likelihood, which is maximised to estimate the 6D camera pose. Additionally, a Markov Chain Monte Carlo (MCMC) algorithm is used to estimate the uncertainty of the offset. Tested on two different platforms, the method was able to estimate the pose to within 0.06 m / 1.05$^{\circ}$ and 0.18 m / 2.39$^{\circ}$. We also propose several approaches to displaying and interpreting the 6D results in a human readable way.Comment: Published in MDPI Sensors, 30 October 201

An under-ice hyperspectral and RGB imaging system to capture fine-scale biophysical properties of sea ice

Sea-ice biophysical properties are characterized by high spatio-temporal variability ranging from the meso- to the millimeter scale. Ice coring is a common yet coarse point sampling technique that struggles to capture such variability in a non-invasive manner. This hinders quantification and understanding of ice algae biomass patchiness and its complex interaction with some of its sea ice physical drivers. In response to these limitations, a novel under-ice sled system was designed to capture proxies of biomass together with 3D models of bottom topography of land-fast sea-ice. This system couples a pushbroom hyperspectral imaging (HI) sensor with a standard digital RGB camera and was trialed at Cape Evans, Antarctica. HI aims to quantify per-pixel chlorophyll-a content and other ice algae biological properties at the ice-water interface based on light transmitted through the ice. RGB imagery processed with digital photogrammetry aims to capture under-ice structure and topography. Results from a 20 m transect capturing a 0.61 m wide swath at sub-mm spatial resolution are presented. We outline the technical and logistical approach taken and provide recommendations for future deployments and developments of similar systems. A preliminary transect subsample was processed using both established and novel under-ice bio-optical indices (e.g., normalized difference indexes and the area normalized by the maximal band depth) and explorative analyses (e.g., principal component analyses) to establish proxies of algal biomass. This first deployment of HI and digital photogrammetry under-ice provides a proof-of-concept of a novel methodology capable of delivering non-invasive and highly resolved estimates of ice algal biomass in-situ, together with some of its environmental drivers. Nonetheless, various challenges and limitations remain before our method can be adopted across a range of sea-ice conditions. Our work concludes with suggested solutions to these challenges and proposes further method and system developments for future research

Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture

This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy

Airborne Hyperspectral Imaging of Lakes

In a time of rising concern about climate change and pollution, the water quality of large lakes acts as an indicator of the health of the environment. To study the water quality at a large scale - up to several hundreds of kilometres - hyperspectral remote sensing is emerging as the main solution. Indeed, different quantities relevant to water quality, like turbidity or concentratrion in chlorophyll-a, can be measured using the spectral reflectance of the water column. Additionally, airborne and spaceborne sensors can cover large areas, thus allowing to study the water at a much larger scale than when simply taking water samples at specific points. Airborne hyperspectral imaging, in particular, offers an acceptable ground resolution - around a metre - which allows to map relevant quantities precisely. However, few existing projects deliver maps that have both a sufficient ground resolution and a large coverage. Furthermore, most existing sensors do not offer a fine spectral resolution, which is for instance crucial when studying the presence of chlorophyll-a, which can only be detected in a narrow range of the electromagnetic spectrum. This thesis presents our work with a hyperspectral sensor developed and used by the Geodetic Engineering Laboratory of EPFL in the Léman-Baïkal project, a cooperative work which aimed at studying both Lake Geneva (Switzerland) and Lake Baikal (Russia). The project included ultralight plane flights with an onboard pushbroom scanner, which allowed to collect data over large areas with a fine spectral resolution. Alongside the use of this sensor came problematics which are at the centre of this thesis: the georeferencing of the scan lines, their radiometric calibration, their analysis and the softwaremanagement of this data. In the following, we present a new method to georeference pushbroom scan lines that uses co-acquired frame images to perform coregistration and to achieve a georeferencing, which RMSE is up to 20 times smaller than the direct one. We propose an efficient radiometric self-calibration method to convert the sensor output to water-leaving reflectance; this method makes use of the visible peaks of atmospheric absorption to align the spectral bands with those of a reference acquisition, and uses the near infrared properties of deep water and vegetation to performabsolute calibration. The last part of the processing - the software management, including data compression - was solved by developing a software called HYPerspectral Orthorectification Software (HypOS). This software is the synthesis of our work, including the tools to performgeometric correction, radiometric calibration and data compression of our hyperspectral data. Two examples of applications are given: the first one deals with mapping chlorophyll-a in the Rhone Delta of Lake Geneva; the second, at a larger scale, uses satellite data to monitor ice coverage over large lakes like Onega or Ladoga (Russia)

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc