In-lab characterization of HYPSOS, a novel stereo hyperspectral observing system: first results

HYPSOS (HYPerspectral Stereo Observing System, patented) is a novel remote sensing instrument able to extract the spectral information from the two channels of a pushbroom stereo camera; thus it simultaneously provides 4D information, spatial and spectral, of the observed features. HYPSOS has been designed to be a compact instrument, compatible with small satellite applications, to be suitable both for planetary exploration as well for terrestrial environmental monitoring. An instrument with such global capabilities, both in terms of scientific return and needed resources, is optimal for fully characterizing the observed surface of investigation. HYPSOS optical design couples a pair of folding mirrors to a modified three mirror anastigmat telescope for collecting the light beams from the optical paths of the two stereo channels; then, on the telescope focal plane, there is the entrance slit of an imaging spectrograph, which selects and disperses the light from the two stereo channels on a bidimensional detector. With this optical design, the two stereo channels share the large majority of the optical elements: this allowed to realize a very compact instrument, which needs much less resources than an equivalent system composed by a stereo camera and a spectrometer. To check HYPSOS actual performance, we realized an instrument prototype to be operated in a laboratory environment. The laboratory setup is representative of a possible flight configuration: the light diffused by a surface target is collimated on the HYPSOS channel entrance apertures, and the target is moved with respect to the instrument to reproduce the in- flight pushbroom acquisition mode. Here we describe HYPSOS and the ground support equipment used to characterize the instrument, and show the preliminary results of the instrument alignment activities

Near real-time stereo vision system

The apparatus for a near real-time stereo vision system for use with a robotic vehicle is described. The system is comprised of two cameras mounted on three-axis rotation platforms, image-processing boards, a CPU, and specialized stereo vision algorithms. Bandpass-filtered image pyramids are computed, stereo matching is performed by least-squares correlation, and confidence ranges are estimated by means of Bayes' theorem. In particular, Laplacian image pyramids are built and disparity maps are produced from the 60 x 64 level of the pyramids at rates of up to 2 seconds per image pair. The first autonomous cross-country robotic traverses (of up to 100 meters) have been achieved using the stereo vision system of the present invention with all computing done onboard the vehicle. The overall approach disclosed herein provides a unifying paradigm for practical domain-independent stereo ranging

Blending Learning and Inference in Structured Prediction

In this paper we derive an efficient algorithm to learn the parameters of structured predictors in general graphical models. This algorithm blends the learning and inference tasks, which results in a significant speedup over traditional approaches, such as conditional random fields and structured support vector machines. For this purpose we utilize the structures of the predictors to describe a low dimensional structured prediction task which encourages local consistencies within the different structures while learning the parameters of the model. Convexity of the learning task provides the means to enforce the consistencies between the different parts. The inference-learning blending algorithm that we propose is guaranteed to converge to the optimum of the low dimensional primal and dual programs. Unlike many of the existing approaches, the inference-learning blending allows us to learn efficiently high-order graphical models, over regions of any size, and very large number of parameters. We demonstrate the effectiveness of our approach, while presenting state-of-the-art results in stereo estimation, semantic segmentation, shape reconstruction, and indoor scene understanding

How to use magnetic field information for coronal loop identification?

The structure of the solar corona is dominated by the magnetic field because the magnetic pressure is about four orders of magnitude higher than the plasma pressure. Due to the high conductivity the emitting coronal plasma (visible e.g. in SOHO/EIT) outlines the magnetic field lines. The gradient of the emitting plasma structures is significantly lower parallel to the magnetic field lines than in the perpendicular direction. Consequently information regarding the coronal magnetic field can be used for the interpretation of coronal plasma structures. We extrapolate the coronal magnetic field from photospheric magnetic field measurements into the corona. The extrapolation method depends on assumptions regarding coronal currents, e.g. potential fields (current free) or force-free fields (current parallel to magnetic field). As a next step we project the reconstructed 3D magnetic field lines on an EIT-image and compare with the emitting plasma structures. Coronal loops are identified as closed magnetic field lines with a high emissivity in EIT and a small gradient of the emissivity along the magnetic field.Comment: 14 pages, 3 figure