Evaluation of Color Anomaly Detection in Multispectral Images For Synthetic Aperture Sensing

In this article, we evaluate unsupervised anomaly detection methods in multispectral images obtained with a wavelength-independent synthetic aperture sensing technique, called Airborne Optical Sectioning (AOS). With a focus on search and rescue missions that apply drones to locate missing or injured persons in dense forest and require real-time operation, we evaluate runtime vs. quality of these methods. Furthermore, we show that color anomaly detection methods that normally operate in the visual range always benefit from an additional far infrared (thermal) channel. We also show that, even without additional thermal bands, the choice of color space in the visual range already has an impact on the detection results. Color spaces like HSV and HLS have the potential to outperform the widely used RGB color space, especially when color anomaly detection is used for forest-like environments.Comment: 12 pages, 6 figures, 3 table

Synthetic Aperture Anomaly Imaging

Previous research has shown that in the presence of foliage occlusion, anomaly detection performs significantly better in integral images resulting from synthetic aperture imaging compared to applying it to conventional aerial images. In this article, we hypothesize and demonstrate that integrating detected anomalies is even more effective than detecting anomalies in integrals. This results in enhanced occlusion removal, outlier suppression, and higher chances of visually as well as computationally detecting targets that are otherwise occluded. Our hypothesis was validated through both: simulations and field experiments. We also present a real-time application that makes our findings practically available for blue-light organizations and others using commercial drone platforms. It is designed to address use-cases that suffer from strong occlusion caused by vegetation, such as search and rescue, wildlife observation, early wildfire detection, and sur-veillance

Stereoscopic Depth Perception Through Foliage

Both humans and computational methods struggle to discriminate the depths of objects hidden beneath foliage. However, such discrimination becomes feasible when we combine computational optical synthetic aperture sensing with the human ability to fuse stereoscopic images. For object identification tasks, as required in search and rescue, wildlife observation, surveillance, and early wildfire detection, depth assists in differentiating true from false findings, such as people, animals, or vehicles vs. sun-heated patches at the ground level or in the tree crowns, or ground fires vs. tree trunks. We used video captured by a drone above dense woodland to test users' ability to discriminate depth. We found that this is impossible when viewing monoscopic video and relying on motion parallax. The same was true with stereoscopic video because of the occlusions caused by foliage. However, when synthetic aperture sensing was used to reduce occlusions and disparity-scaled stereoscopic video was presented, whereas computational (stereoscopic matching) methods were unsuccessful, human observers successfully discriminated depth. This shows the potential of systems which exploit the synergy between computational methods and human vision to perform tasks that neither can perform alone

Quantification of contaminants associated with LDEF

The quantification of contaminants on the Long Duration Exposure Facility (LDEF) and associated hardware or tools is addressed. The purpose of this study was to provide a background data base for the evaluation of the surface of the LDEF and the effects of orbital exposure on that surface. This study necessarily discusses the change in the distribution of contaminants on the LDEF with time and environmental exposure. Much of this information may be of value for the improvement of contamination control procedures during ground based operations. The particulate data represents the results of NASA contractor monitoring as well as the results of samples collected and analyzed by the authors. The data from the tapelifts collected in the Space Shuttle Bay at Edwards Air Force Base and KSC are also presented. The amount of molecular film distributed over the surface of the LDEF is estimated based on measurements made at specific locations and extrapolated over the surface area of the LDEF. Some consideration of total amount of volatile-condensible materials available to form the resultant deposit is also presented. All assumptions underlying these estimates are presented along with the rationale for the conclusions. Each section is presented in a subsection for particles and another for molecular films

Factors Affecting Accuracy and Precision in Measuring Material Surfaces

The fractal dimensions of material surfaces are of interest because they can be related to material performance. Such surfaces include the fracture surfaces of broken specimens, surfaces abraded by airborne particles, and surfaces upon which coatings of another material have been applied. Scientists who study the fracture surfaces of failed medical implants stand to benefit greatly from fractal analysis. The origin of failure is often damaged or lost during retrieval of a failed implant, and evaluation of the undamaged portions of the fracture surface by relying on the self-similarity property of fractals may allow one to deduce the conditions that were present at the failure origin at the moment of failure. If the analysis of material surfaces will be used as an engineering tool, then it is important to identify the analysis methods that yield the most precise and accurate estimates of surface dimension. Eleven algorithms for calculating the surface dimension are compared. A method for correcting the bias of dimension estimates is presented. The sources of error involved in atomic force microscopy, optical microscopy, mechanical sectioning, and fabrication of specimen replicas are discussed

A multi-object spectral imaging instrument

We have developed a snapshot spectral imaging system which fits onto the side camera port of a commercial inverted microscope. The system provides spectra, in real time, from multiple points randomly selected on the microscope image. Light from the selected points in the sample is directed from the side port imaging arm using a digital micromirror device to a spectrometer arm based on a dispersing prism and CCD camera. A multi-line laser source is used to calibrate the pixel positions on the CCD for wavelength. A CMOS camera on the front port of the microscope allows the full image of the sample to be displayed and can also be used for particle tracking, providing spectra of multiple particles moving in the sample. We demonstrate the system by recording the spectra of multiple fluorescent beads in aqueous solution and from multiple points along a microscope sample channel containing a mixture of red and blue dye