UCalib: Cameras Autocalibration on Coastal Video Monitoring Systems

Following the path set out by the “Argus” project, video monitoring stations have become a very popular low cost tool to continuously monitor beaches around the world. For these stations to be able to offer quantitative results, the cameras must be calibrated. Cameras are typically calibrated when installed, and, at best, extrinsic calibrations are performed from time to time. However, intra-day variations of camera calibration parameters due to thermal factors, or other kinds of uncontrolled movements, have been shown to introduce significant errors when transforming the pixels to real world coordinates. Departing from well-known feature detection and matching algorithms from computer vision, this paper presents a methodology to automatically calibrate cameras, in the intra-day time scale, from a small number of manually calibrated images. For the three cameras analyzed here, the proposed methodology allows for automatic calibration of >90% of the images in favorable conditions (images with many fixed features) and ∼40% in the worst conditioned camera (almost featureless images). The results can be improved by increasing the number of manually calibrated images. Further, the procedure provides the user with two values that allow for the assessment of the expected quality of each automatic calibration. The proposed methodology, here applied to Argus-like stations, is applicable e.g., in CoastSnap sites, where each image corresponds to a different camera.

Follow-Up and Validation of K2 and TESS Planetary Systems With Keck NIRC2 Adaptive Optics Imaging

High resolution imaging (HRI) is a critical part of the transiting exoplanet follow-up and validation process. HRI allows previously unresolved stellar companions and background blends to be resolved, vetting false positive signals and improving the radii measurements of true planets. Through a multi-semester Keck NIRC2 adaptive optics imaging program, we have pursued HRI of K2 and TESS candidate planet host systems to provide the transiting exoplanet community with necessary data for system validation and characterization. Here we present a summary of our ongoing program that includes an up to date list of targets observed, a description of the observations and data reduction, and a discussion of planetary systems validated by the community using these data. This observing program has been key in NASA's K2 and TESS missions reaching their goals of identifying new exoplanets ideal for continued follow-up observations to measure their masses and investigate their atmospheres. Once processed, all observations presented here are available as calibrated images and resulting contrast curves through the Exoplanet Follow-up Observing Program (ExoFOP) website. We encourage members of the exoplanet community to use these data products in their ongoing planetary system validation and characterization efforts.

Variations in spectral reflectivity and vertical cloud structure of Jupiter’s Great Red Spot

<p>The Great Red Spot (GRS) of Jupiter is a large anticyclonic vortex present in the Jovian atmosphere. First observed in the XVII century, it is almost constantly located at 22°S and it is arguably one of the main atmospheric phenomena in the Solar System. Despite having been widely studied, the nature of the chromophore species that provide its characteristic colour to the GRS’s upper clouds and hazes is still unclear, as well as its creation and destruction mechanisms.</p><p>In this work we have analysed images provided by the Hubble Space Telescope’s Wide Field Camera 3 between 2015 and 2019, with a spectral coverage from the ultraviolet to the near infrared, including two methane absorption bands. These images have undergone a photometric process of cross calibration, ensuring a consistent correlation among the images corresponding to different visits and years. From such calibrated images, we have obtained the spectral reflectivity of the GRS and its surroundings, with particular emphasis on a few, dynamically interesting regions.</p><p>We used the NEMESIS radiative transfer suite to retrieve the main atmospheric parameters (particle vertical and size distributions, refractive indices…) that are able to explain the observed spectral reflectivity of the selected regions. Here we report the spatial and temporal variations on such parameters and their implications on the GRS overall dynamics.</p>

Smart Artificial Markers for Accurate Visual Mapping and Localization

Artificial marker mapping is a useful tool for fast camera localization estimation with a certain degree of accuracy in large indoor and outdoor environments. Nonetheless, the level of accuracy can still be enhanced to allow the creation of applications such as the new Visual Odometry and SLAM datasets, low-cost systems for robot detection and tracking, and pose estimation. In this work, we propose to improve the accuracy of map construction using artificial markers (mapping method) and camera localization within this map (localization method) by introducing a new type of artificial marker that we call the smart marker. A smart marker consists of a square fiducial planar marker and a pose measurement system (PMS) unit. With a set of smart markers distributed throughout the environment, the proposed mapping method estimates the markers’ poses from a set of calibrated images and orientation/distance measurements gathered from the PMS unit. After this, the proposed localization method can localize a monocular camera with the correct scale, directly benefiting from the improved accuracy of the mapping method. We conducted several experiments to evaluate the accuracy of the proposed methods. The results show that our approach decreases the Relative Positioning Error (RPE) by 85% in the mapping stage and Absolute Trajectory Error (ATE) by 50% for the camera localization stage in comparison with the state-of-the-art methods present in the literature.

Very high-resolution terrain surveys of the Chã das Caldeiras lava fields (Fogo Island, Cape Verde)

Fogo in the Cape Verde archipelago off Western Africa is one of the most prominent and active ocean island volcanoes on Earth, posing an important hazard to both local populations and at a regional level. The last eruption took place between 23 November 2014 and 8 February 2015 in the Chã das Caldeiras area at an elevation close to 1,800 m above sea level The eruptive episode gave origin to extensive lava flows that almost fully destroyed the settlements of Bangaeira, Portela and Ilhéu de Losna. In December 2016 a survey of the Chã das Caldeiras area was conducted using a fixed-wing unmanned aerial vehicle and RTK GNSS, with the objective of improving the mapping accuracy derived from satellite platforms. The main result is an ultra-high resolution 3D point cloud with a Root Mean Square Error of 0.08 m in X, 0.11 m in Y and 0.12 m in Z, which provides unprecedented accuracy. The survey covers an area of 23.9 km2 and used 2909 calibrated images with an average ground sampling distance of 7.2 cm. A digital surface model and an orthomosaic with 25 cm resolution are provided, together with elevation contours with an equidistance of 50 cm and a 3D texture mesh for visualization purposes. The delineation of the 2014–15 lava flows shows an area of 4.53 km2 by lava, which is smaller but more accurate than the previous estimates from 4.8 to 4.97 km2. The difference in the calculated area, when compared to previously reported values, is due to a more detailed mapping of flow geometry and the exclusion of the areas corresponding to kīpukas. Our study provides an ultra high-resolution dataset of the areas affected by Fogo's latest eruption – crucial for local planning – and provides a case study to determine the advantages of ultra high-resolution UAV surveys in disaster-prone areas. The dataset is available for download at http://doi.org/10.5281/zenodo.4035038 (Vieira et al., 2020).

Climate is a strong predictor of near-infrared reflectance but a poor predictor of colour in butterflies

Colour variation across climatic gradients is a common ecogeographical pattern; yet there is long-standing contention over underlying causes, particularly selection for thermal benefits. We tested the evolutionary association between climate gradients and reflectance of near-infrared (NIR) wavelengths, which influence heat gain but are not visible to animals. We measured ultraviolet (UVA), visible (Vis) and NIR reflectance from calibrated images of 372 butterfly specimens from 60 populations (49 species, five families) spanning the Australian continent. Consistent with selection for thermal benefits, the association between climate and reflectance was stronger for NIR than UVA–Vis wavelengths. Furthermore, climate predicted reflectance of the thorax and basal wing, which are critical to thermoregulation; but it did not predict reflectance of the entire wing, which has a variable role in thermoregulation depending on basking behaviour. These results provide evidence that selection for thermal benefits has shaped the reflectance properties of butterflies.

Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation

In the image processing pipeline of almost every digital camera, there is a part for removing the influence of illumination on the colors of the image scene. Tuning the parameter values of an illumination estimation method for maximal accuracy requires calibrated images with known ground-truth illumination, but creating them for a given sensor is time-consuming. In this paper, the green stability assumption is proposed that can be used to fine-tune the values of some common illumination estimation methods by using only non-calibrated images. The obtained accuracy is practically the same as when training on calibrated images, but the whole process is much faster since calibration is not required and thus time is saved. The results are presented and discussed. The source code website is provided in Section Experimental Results.