A novel satellite mission concept for upper air water vapour, aerosol and cloud observations using integrated path differential absorption LiDAR limb sounding

We propose a new satellite mission to deliver high quality measurements of upper air water vapour. The concept centres around a LiDAR in limb sounding by occultation geometry, designed to operate as a very long path system for differential absorption measurements. We present a preliminary performance analysis with a system sized to send 75 mJ pulses at 25 Hz at four wavelengths close to 935 nm, to up to 5 microsatellites in a counter-rotating orbit, carrying retroreflectors characterized by a reflected beam divergence of roughly twice the emitted laser beam divergence of 15 µrad. This provides water vapour profiles with a vertical sampling of 110 m; preliminary calculations suggest that the system could detect concentrations of less than 5 ppm. A secondary payload of a fairly conventional medium resolution multispectral radiometer allows wide-swath cloud and aerosol imaging. The total weight and power of the system are estimated at 3 tons and 2,700 W respectively. This novel concept presents significant challenges, including the performance of the lasers in space, the tracking between the main spacecraft and the retroreflectors, the refractive effects of turbulence, and the design of the telescopes to achieve a high signal-to-noise ratio for the high precision measurements. The mission concept was conceived at the Alpbach Summer School 2010

Polarimetric remote sensing system analysis: Digital Imaging and Remote Sensing Image Generation (DIRSIG) model validation and impact of polarization phenomenology on material discriminability

In addition to spectral information acquired by traditional multi/hyperspectral systems, passive electro optical and infrared (EO/IR) polarimetric sensors also measure the polarization response of different materials in the scene. Such an imaging modality can be useful in improving surface characterization; however, the characteristics of polarimetric systems have not been completely explored by the remote sensing community. Therefore, the main objective of this research was to advance our knowledge in polarimetric remote sensing by investigating the impact of polarization phenomenology on material discriminability. The first part of this research focuses on system validation, where the major goal was to assess the fidelity of the polarimetric images simulated using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. A theoretical framework, based on polarization vision models used for animal vision studies and industrial defect detection applications, was developed within which the major components of the polarimetric image chain were validated. In the second part of this research, a polarization physics based approach for improved material discriminability was proposed. This approach utilizes the angular variation in the polarization response to infer the physical characteristics of the observed surface by imaging the scene in three different view directions. The usefulness of the proposed approach in improving detection performance in the absence of apriori knowledge about the target geometry was demonstrated. Sensitivity analysis of the proposed system for different scene related parameters was performed to identify the imaging conditions under which the material discriminability is maximized. Furthermore, the detection performance of the proposed polarimetric system was compared to that of the hyperspectral system to identify scenarios where polarization information can be very useful in improving the target contrast

Design of equipment for lunar dust removal

NASA has a long range goal of constructing a fully equipped, manned lunar base on the near side of the moon by the year 2015. During the Apollo Missions, lunar dust coated and fouled equipment surfaces and mechanisms exposed to the lunar environment. In addition, the atmosphere and internal surfaces of the lunar excursion module were contaminated by lunar dust which was brought in on articles passed through the airlock. Consequently, the need exists for device or appliance to remove lunar dust from surfaces of material objects used outside of the proposed lunar habitat. Additionally, several concepts were investigated for preventing the accumulation of lunar dust on mechanisms and finished surfaces. The character of the dust and the lunar environment present unique challenges for the removal of contamination from exposed surfaces. In addition to a study of lunar dust adhesion properties, the project examines the use of various energy domains for removing the dust from exposed surfaces. Also, prevention alternatives are examined for systems exposed to lunar dust. A concept utilizing a pressurized gas is presented for dust removal outside of an atmospherically controlled environment. The concept consists of a small astronaut/robotic compatible device which removes dust from contaminated surfaces by a small burst of gas

Deep Sea Robotic Imaging Simulator

Nowadays underwater vision systems are being widely applied in ocean research. However, the largest portion of the ocean - the deep sea - still remains mostly unexplored. Only relatively few image sets have been taken from the deep sea due to the physical limitations caused by technical challenges and enormous costs. Deep sea images are very different from the images taken in shallow waters and this area did not get much attention from the community. The shortage of deep sea images and the corresponding ground truth data for evaluation and training is becoming a bottleneck for the development of underwater computer vision methods. Thus, this paper presents a physical model-based image simulation solution, which uses an in-air texture and depth information as inputs, to generate underwater image sequences taken by robots in deep ocean scenarios. Different from shallow water conditions, artificial illumination plays a vital role in deep sea image formation as it strongly affects the scene appearance. Our radiometric image formation model considers both attenuation and scattering effects with co-moving spotlights in the dark. By detailed analysis and evaluation of the underwater image formation model, we propose a 3D lookup table structure in combination with a novel rendering strategy to improve simulation performance. This enables us to integrate an interactive deep sea robotic vision simulation in the Unmanned Underwater Vehicles simulator. To inspire further deep sea vision research by the community, we release the source code of our deep sea image converter to the public (https://www.geomar.de/en/omv-research/robotic-imaging-simulator)