A feasibility study and the mission definition for a Quantum Key Distribution from a geostationary satellite have been successfully completed by a Spanish consortium led by HISPASAT under ARTES ESA funding. The system will be a hosted payload of a regular communication satellite mission. In this work, the conceptual optical design of the payload is presented. It includes the trade-off between an on-axis telescope with central obscuration and an off-axis unobscured telescope. Besides, the optical design of the transmission (i.e.: quantum signal and downlink beacon) and reception channels (i.e.: uplink beacon) are described and the critical subsystems identified. A polarization control system based on liquid-crystals, which avoids mechanisms, is used to correct the errors due to instrumental residual polarization, reference system rotation or long-term instability of the QKD source during operation.
For the remote detection of chiral amino acids to be feasible, it is required to develop a high sensitivity spectropolarimeter susceptible to detect the signature of the Optical Rotatory Dispersion (ORD) produced by alanine in the 160-200 nm spectral range. This instrument is part of the payload of the Ultraviolet Researcher to Investigate the Emergence of Life (URIEL), a small size (50 cm primary) space telescope designed to carry out low dispersion (600-1,000) UV spectropolarimetry in the 140-350 nm spectral range. In this contribution, we describe the design of the spectropolarimeter.
The mission Ultraviolet Researcher to Investigate the Emergence of Life (URIEL) is designed to carry out low dispersion (600-1,000) UV spectropolarimetry in the 140-400 nm spectral range to investigate the formation of planetary systems, its interaction with stellar winds and search for signatures of prebiotic molecules by remote sensing of small bodies in the Solar System (comets and meteorites) in near Earth orbit. URIEL is conceived as a 50cm primary telescope with a RitcheyChrétien mounting. The telescope is equipped with a single instrument, the ultraviolet spectropolarimeter, whose low dispersion will enable resolving the main spectral features whilst guaranteeing enough flux per resolution element for the Stokes parameters to be measured to an accuracy of 500 ppm in the full range. According to recent calculations based on the chemical analysis of meteorites, this accuracy suffices for the remote detection of alanine by its optical activity at 180 nm in nearby minor bodies. In this sense, URIEL is a pathfinder mission to the technology that will enable remote sensing of amino acids and addressing the source of the chirality imbalance in Earth's bio-molecules.
OUL is a wide field imager designed as a small, additional payload to be attached to the Luna 26 mission. The instrument has a field of view of 20° × 20° and provides images with angular resolution 3 arcmin in several far ultraviolet bands, including Lyman-α, He II at 164nm and several continuum bands. The imager is designed to monitor the Earth’s exosphere and the ecliptic (+/-20°) primary at Lyman-α and in the 125-140 nm and 145-170 nm bands. In this contribution, the optical design of the instrument, its mechanical layout and the science program to be implemented will be described.
There is a growing interest in lunar exploration fed by the perception that the Moon can be made accessible to low-cost missions in the next decade. The ongoing projects to set a communications relay in lunar orbit and a deep space gateway, as well as the spreading of commercial-of-the shelf technology for small space platforms such as the cubesats contribute to this perception. Small, cubesat size satellites orbiting the Moon offer ample opportunities to study the Moon and enjoy an advantage point to monitor the Solar System and the large-scale interaction between the Earth and the solar wind. We describe the technical characteristics of a 12U cubesat to be set in polar lunar orbit for this purpose and the science behind it. The mission is named Earth as an exoplanet (EarthASAP) and is submitted to the Lunar Cubesats for Exploration call in 2016. EarthASAP is designed to monitor hydrated rock reservoirs in the lunar poles and to study the interaction between the large Earth’s exosphere and the solar wind in preparation for future exoplanetary missions.
KEYWORDS: Modulation transfer functions, Sensors, Cameras, Telescopes, Distortion, Signal to noise ratio, Data modeling, Simulation of CCA and DLA aggregates, Spectral resolution, Mirrors
Ingenio/SEOSAT is a high-spatial-resolution optical mission developed under the Spanish Earth Observation National Program for Satellites (PNOTS), and managed technically in the framework of an ESA contract. It features as Primary Payload (PP) a high-resolution optical payload with one 2.5 meter resolution panchromatic channel and four 10 meter resolution visible/near infrared spectral channels. It is based on a twin Korsch telescope concept, each telescope covering half of the instrument’s swath width. At the present stage, the principal payload has undergone the vibration and environmental tests, and the final performance test campaign has been completed successfully. In this communication, we will present the main measured optical performance parameters, and its relation to predictions obtained from the different computer models. First, the payload’s geometric performance is addressed in the paper, with focus on parameters such as the spatial sampling angle, detection line angle and distortion. On a second group, wavefront error and modulation transfer function are reviewed. Finally, radiometric performance is considered, with parameters such as radiance saturation levels and signal-to-noise ratios at defined minimum and reference radiances. All instrument performances have been measured at Thales Alenia Space in Cannes with set-ups developed specifically by Thales Alenia Space for Ingenio/SEOSAT ( i.e. Modulation Transfer function, straylight and radiometric measurements).
SEOSAT/Ingenio (Spanish Earth Observation SATellite) is a high-spatial-resolution optical mission procured by the European Space Agency on behalf of and funded through the Spanish program authority CDTI. The Seosat/Ingenio mission is part of the Spanish Earth Observation National Program for Satellites (PNOTS). The mission is devoted to provide land and coastal zone optical images (panchromatic and multispectral) for applications in cartography, land use and mapping, urban management, costal management, agriculture monitoring, precision agriculture, water management, environmental monitoring, risk management and security and is a potential contributor to the European Copernicus program.
The SEOSAT/Ingenio satellite will operate from a polar-heliosynchronous orbit at 670 km of altitude and has an imaging capability up to 2.5 Mkm2 per day, with world-wide accessibility in less than 3 days and a design lifetime of 7 years. The satellite is based on an Astrobus-M platform architecture weighing about 800 kg and with 580 W installed power and is compatible with a launch with Vega.
The Primary Payload is a push-broom imager, observing simultaneously in a Panchromatic band with 2.5 m resolution and in 4 multispectral bands (B,G,R and NIR) with 10 m resolution, over a swath of 55 km. Bands are co-registered at 1/10 of the pixel and geo-located at subpixel level in post-processing. The Optical design relies on two Korsch on-axis 250 mm aperture telescopes with intermediate imaging plane, in-field spectral separation and staggered-detectors focal planes. The detection system is based on CCD’s (with TDI operation for the PAN) and has MS color filters with direct deposition of the pass bands and masks on a single substrate.
The Satellite flight model is undergoing final integration and testing after final characterization and calibration of the Primary Payload . The SEOSAT satellite is expected to be ready for launch by end 2019.
Ingenio/SEOSAT is a high-spatial-resolution optical mission developed under the Spanish Earth Observation National Program for Satellites (PNOTS), and managed technically in the framework of an ESA contract. It features as Primary Payload (PP) a high-resolution optical payload with one 2.5 meter resolution panchromatic channel and four 10 meter resolution visible/near infrared spectral channels. It is based on a twin Korsch telescope concept, each telescope covering half of the instrument’s swath width. In this communication is presented a detailed account of the work performed to accurately characterize and correct by post-processing an image ghost present in the multi-spectral channels of the primary payload. The work reported here includes the description and analysis of the results of three test campaigns, performed at Thales Alenia Space. Tests carried out include radiometric tests at focal plane level, generic stray-light tests and a novel test to characterize specifically the parameters of the studied cross-talk image ghost. Field-dependent point spread functions for the studied image ghost have been generated by optical analysis, and have been adjusted with the results of the performed tests. From these, image filters have been devised to reproduce, and remove by subtraction, the image ghost. Quantitative image ghost correction results on an actual test image are shown.
Ingenio/SEOSAT is a multi-spectral high-resolution optical satellite for Earth remote sensing, designed to provide imagery to different Spanish civil, institutional and governmental users, and potentially to other European users in the frame of GMES and GEOSS.
Ingenio/SEOSAT is a multi-spectral high-resolution optical satellite for Earth remote sensing, designed to provide imagery to different Spanish civil, institutional and governmental users, and potentially to other European users in the frame of GMES and GEOSS. In this communication is presented the developed shimming procedure for the light-weighted primary mirror (M1) of the Ingenio/SEOSAT telescope, together with obtained results. The shimming operation has been devised to accurately cancel the residual deformation on the mirror surface caused by its integration on the telescope structure. This deformation is generally small but not necessarily negligible; even if all elements are integrated using proper isostatic mounts.
C. Miravet, D. Zorita, J. Bueno, L. Pascual, A. García Marín, G. Taubmann, J. Azcona, J. Arroyo, I. Monasterio, U. García, J. Martin, C. Mas, J. Muñoz, A. Lopez, J. Eguía, S. Jarabo, R. García, R. Navarro, T. Belenguer, L. González, C. Pastor, D. Arrazola, C. Gonzalez Alvarado, I. Cabeza, A. Borges, A. Marini, G. Crippa
Ingenio/SEOSAT is the flagship mission for the Spanish Space Plan 2007-2011, as is currently under development by a
Spanish industrial consortium in the framework of an ESA contract. Ingenio/SEOSAT is a multi-spectral high-resolution
optical satellite for Earth Remote Sensing, designed to provide imagery to different Spanish civil, institutional and
governmental users, and potentially to other European users in the frame of GMES and GEOSS. SEOSAT/Ingenio is a
Low Earth Orbiting mission. It features a Primary Payload (PP) with one 2.5 meter resolution panchromatic channel and
four 10 meter resolution visible/near infrared spectral channels. The PP swath close to 55 km ensures a frequent revisit
period, and offers quick accessibility to any point on Earth in emergency situations. In this paper are described the main
characteristics and development status of the instrument from an opto-mechancial point of view, as well as the estimated performance data.
This paper shows how local directional entropy can be used as a tool to build up a robust local image descriptor for
image feature extraction. Among other possible choices, the Rényi entropy has been selected as the main technique for
this application. Local directional entropy which is related with the anisotropy images has been considered here as the
basis for the design of a new Rényi entropy-based local image descriptor (RELID). The properties of this new descriptor
are described and evaluated. The experimental results confirm that the new descriptor is endowed by most of the
invariant properties desired for object recognition applications.
Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and
tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and
instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy,
and may exhibit insuffcient spatial and temporal resolution. In particular, several external effects blur images.
Techniques for recovering the original image include blind deconvolution (to remove blur) and superresolution
(SR). The stability of these methods depends on having more than one image of the same frame. Differences
between images are necessary to provide new information, but they can be almost unperceivable. State-of-the-art
SR techniques achieve remarkable results in resolution enhancement by estimating the subpixel shifts between
images, but they lack any apparatus for calculating the blurs. In this paper, after introducing a review of
current SR techniques we describe two recently developed SR methods by the authors. First, we introduce a
variational method that minimizes a regularized energy function with respect to the high resolution image and
blurs. In this way we establish a unifying way to simultaneously estimate the blurs and the high resolution
image. By estimating blurs we automatically estimate shifts with subpixel accuracy, which is inherent for good
SR performance. Second, an innovative learning-based algorithm using a neural architecture for SR is described.
Comparative experiments on real data illustrate the robustness and utilization of both methods.
In this paper, a decision support system for ship identification is presented. The system receives as input a silhouette of the vessel to be identified, previously extracted from a side view of the object. This view could have been acquired with imaging sensors operating at different spectral ranges (CCD, FLIR, image intensifier). The input silhouette is preprocessed and compared to those stored in a database, retrieving a small number of potential matches ranked by their similarity to the target silhouette. This set of potential matches is presented to the system operator, who makes the final ship identification. This system makes use of an evolved version of the Curvature Scale Space (CSS) representation. In the proposed approach, it is curvature extrema, instead of zero crossings, that are tracked during silhouette evolution, hence improving robustness and enabling to cope successfully with cases where the standard CCS representation is found to be unstable. Also, the use of local curvature was replaced with the more robust concept of lobe concavity, with significant additional gains in performance. Experimental results on actual operational imagery prove the excellent performance and robustness of the developed method.
Automatic object segmentation in highly noisy image sequences, composed by a translating object over a background having a different motion, is achieved through joint motion-texture analysis. Local motion and/or texture is characterized by the energy of the local spatio-temporal spectrum, as different textures undergoing different translational motions display distinctive features in their 3D (x,y,t) spectra. Measurements of local spectrum energy are obtained using a bank of directional 3rd order Gaussian derivative filters in a multiresolution pyramid in space- time (10 directions, 3 resolution levels). These 30 energy measurements form a feature vector describing texture-motion for every pixel in the sequence. To improve discrimination capability and reduce computational cost, we automatically select those 4 features (channels) that best discriminate object from background, under the assumptions that the object is smaller than the background and has a different velocity or texture. In this way we reject features irrelevant or dominated by noise, that could yield wrong segmentation results. This method has been successfully applied to sequences with extremely low visibility and for objects that are even invisible for the eye in absence of motion.
Hispars is an European EUCLID investigation projected devoted to evaluation of Artificial Neural Networks for defense pattern applications. Three demonstrators representing three military operational contexts (Air-to- Ground, Ground Battlefield, Naval Threat Evaluation) have been defined and developed. A set of operational processing chains have been selected, and for each of them, ANN methods have been proposed and evaluated on real data set at each level of processing, in comparison to those classical techniques used in existing equipment.
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