Combined person classification with airborne optical sectioning.

Fully autonomous drones have been demonstrated to find lost or injured persons under strongly occluding forest canopy. Airborne optical sectioning (AOS), a novel synthetic aperture imaging technique, together with deep-learning-based classification enables high detection rates under realistic search-and-rescue conditions. We demonstrate that false detections can be significantly suppressed and true detections boosted by combining classifications from multiple AOS-rather than single-integral images. This improves classification rates especially in the presence of occlusion. To make this possible, we modified the AOS imaging process to support large overlaps between subsequent integrals, enabling real-time and on-board scanning and processing of groundspeeds up to 10 m/s.

Airborne Optical Sectioning for Nesting Observation.

We describe how a new and low-cost aerial scanning technique, airborne optical sectioning (AOS), can support ornithologists in nesting observation. After capturing thermal and color images during a seven minutes drone flight over a 40 × 12 m patch of the nesting site of Austria's largest heron population, a total of 65 herons and 27 nests could be identified, classified, and localized in a sparse 3D reconstruction of the forest. AOS is a synthetic aperture imaging technique that removes occlusion caused by leaves and branches. It registers recorded images to a common 3D coordinate system to support the reconstruction and analysis of the entire forest volume, which is impossible with conventional 2D or 3D imaging techniques. The recorded data is published with open access.

Synthetic Aperture Imaging With Drones.

Synthetic apertures sample the signal of wide aperture sensors with either arrays of static or single moving smaller aperture sensors whose individual signals are computationally combined to increase the resolution, depth-of-field, frame rate, contrast, and signal-to-noise ratio. This principle has been used for radar, telescopes, microscopes, sonar, ultrasound, laser, and optical imaging. With airborne optical sectioning (AOS), we apply camera drones for synthetic aperture imaging to uncover the ruins of a 19th century fortification system that is concealed by dense forest and shrubs. Compared to alternative airborne scanning technologies (such as LiDAR), AOS is cheaper, delivers surface color information, achieves higher sampling resolutions, and (in contrast to photogrammetry) does not suffer from inaccurate correspondence matches and long processing times.

Micro-lens aperture array for enhanced thin-film imaging using luminescent concentrators.

We present a 300 µm thick optical Söller collimator realized by X-ray lithography on a PMMA wafer which, when paired with luminescent concentrator films, forms the first complete prototype of a short-distance, flexible, scalable imaging system that is less than 1 mm thick. We describe two ways of increasing the light-gathering ability of the collimator by using hexagonal aperture cells and embedded micro-lenses, evaluate a new micro-lens aperture array (MLAA) for proof of concept, and analyze the optical imaging properties of flexible MLAAs when realized as thin films.

Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection.

We report the application of a hidden Markov model (HMM) on multifractal tissue optical properties derived via the Born approximation-based inverse light scattering method for effective discrimination of precancerous human cervical tissue sites from the normal ones. Two global fractal parameters, generalized Hurst exponent and the corresponding singularity spectrum width, computed by multifractal detrended fluctuation analysis (MFDFA), are used here as potential biomarkers. We develop a methodology that makes use of these multifractal parameters by integrating with different statistical classifiers like the HMM and support vector machine (SVM). It is shown that the MFDFA-HMM integrated model achieves significantly better discrimination between normal and different grades of cancer as compared to the MFDFA-SVM integrated model.