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.
In this paper we develop a new filtering framework for tensor signal processing using the theory of vector spaces. From this point of view, signals are regarded as elements of vector spaces and operators as mappings from the input space to the output space. Hence, it is possible to generalize the principle of superposition to any operator defined on the signal spaces. Systems that obey that generalization of the principle of superposition are referred to as homomorphic and they can be decomposed in a cascade of three homomorphic subsystems: the first one operates on the input signal space, the second one is a linear system in the usual sense and the third one operates on the output signal space. Thus, suitable input and output subsystems can be chosen to deal with input signals, which defines a whole family of homomorphic filters. To apply this idea for DT-MRI signals, which consist of positive semi-definite matrices, we identify input and output signal spaces as the set of those real symmetric positive semi-definite matrices. Our homomorphic filtering framework not only guarantees a positive-semidefinite output tensor field whatever linear filter is used to regularize the noisy input, but also reduces the swelling effect produced by a faster regularization of diffusivities rather than orientations, as demonstrate the encouraging results that have been obtained.
We present an anisotropic filtering scheme which uses a nonlinear version of the local structure tensor to dynamically adapt the shape of the neighborhood used to perform the estimation. In this way, only the samples along the orthogonal direction to that of maximum signal variation are chosen to estimate the value at the current position, which helps to better preserve boundaries and structure information. This idea sets the basis of an anisotropic filtering framework which can be applied for different kinds of linear filters, such as Wiener
or LMMSE, among others. In this paper, we describe the underlying idea using anisotropic gaussian filtering which allows us, at the same time, to study the influence of nonlinear structure tensors in filtering schemes, as we compare the performance to that obtained with classical definitions of the structure tensor.
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