14,534 research outputs found
Development and evaluation of a Hadamard transform imaging spectrometer and a Hadamard transform thermal imager
A spectrometric imager and a thermal imager, which achieve multiplexing by the use of binary optical encoding masks, were developed. The masks are based on orthogonal, pseudorandom digital codes derived from Hadamard matrices. Spatial and/or spectral data is obtained in the form of a Hadamard transform of the spatial and/or spectral scene; computer algorithms are then used to decode the data and reconstruct images of the original scene. The hardware, algorithms and processing/display facility are described. A number of spatial and spatial/spectral images are presented. The achievement of a signal-to-noise improvement due to the signal multiplexing was also demonstrated. An analysis of the results indicates both the situations for which the multiplex advantage may be gained, and the limitations of the technique. A number of potential applications of the spectrometric imager are discussed
Breaking new ground in mapping human settlements from space -The Global Urban Footprint-
Today 7.2 billion people inhabit the Earth and by 2050 this number will have
risen to around nine billion, of which about 70 percent will be living in
cities. Hence, it is essential to understand drivers, dynamics, and impacts of
the human settlements development. A key component in this context is the
availability of an up-to-date and spatially consistent map of the location and
distribution of human settlements. It is here that the Global Urban Footprint
(GUF) raster map can make a valuable contribution. The new global GUF binary
settlement mask shows a so far unprecedented spatial resolution of 0.4 arcsec
() that provides - for the first time - a complete picture of the
entirety of urban and rural settlements. The GUF has been derived by means of a
fully automated processing framework - the Urban Footprint Processor (UFP) -
that was used to analyze a global coverage of more than 180,000 TanDEM-X and
TerraSAR-X radar images with 3m ground resolution collected in 2011-2012.
Various quality assessment studies to determine the absolute GUF accuracy based
on ground truth data on the one hand and the relative accuracies compared to
established settlements maps on the other hand, clearly indicate the added
value of the new global GUF layer, in particular with respect to the
representation of rural settlement patterns. Generally, the GUF layer achieves
an overall absolute accuracy of about 85\%, with observed minima around 65\%
and maxima around 98 \%. The GUF will be provided open and free for any
scientific use in the full resolution and for any non-profit (but also
non-scientific) use in a generalized version of 2.8 arcsec ().
Therewith, the new GUF layer can be expected to break new ground with respect
to the analysis of global urbanization and peri-urbanization patterns,
population estimation or vulnerability assessment
Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks
Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector
Quality assessment by region in spot images fused by means dual-tree complex wavelet transform
This work is motivated in providing and evaluating a fusion algorithm of remotely sensed images, i.e. the fusion of a high spatial resolution panchromatic image with a multi-spectral image (also known as pansharpening) using the dual-tree complex wavelet transform (DT-CWT), an effective approach for conducting an analytic and oversampled wavelet transform to reduce aliasing, and in turn reduce shift dependence of the wavelet transform. The proposed scheme includes the definition of a model to establish how information will be extracted from the PAN band and how that information will be injected into the MS bands with low spatial resolution. The approach was applied to Spot 5 images where there are bands falling outside PAN’s spectrum. We propose an optional step in the quality evaluation protocol, which is to study the quality of the merger by regions, where each region represents a specific feature of the image. The results show that DT-CWT based approach offers good spatial quality while retaining the spectral information of original images, case SPOT 5. The additional step facilitates the identification of the most affected regions by the fusion process
Review of high-contrast imaging systems for current and future ground- and space-based telescopes I. Coronagraph design methods and optical performance metrics
The Optimal Optical Coronagraph (OOC) Workshop at the Lorentz Center in
September 2017 in Leiden, the Netherlands gathered a diverse group of 25
researchers working on exoplanet instrumentation to stimulate the emergence and
sharing of new ideas. In this first installment of a series of three papers
summarizing the outcomes of the OOC workshop, we present an overview of design
methods and optical performance metrics developed for coronagraph instruments.
The design and optimization of coronagraphs for future telescopes has
progressed rapidly over the past several years in the context of space mission
studies for Exo-C, WFIRST, HabEx, and LUVOIR as well as ground-based
telescopes. Design tools have been developed at several institutions to
optimize a variety of coronagraph mask types. We aim to give a broad overview
of the approaches used, examples of their utility, and provide the optimization
tools to the community. Though it is clear that the basic function of
coronagraphs is to suppress starlight while maintaining light from off-axis
sources, our community lacks a general set of standard performance metrics that
apply to both detecting and characterizing exoplanets. The attendees of the OOC
workshop agreed that it would benefit our community to clearly define
quantities for comparing the performance of coronagraph designs and systems.
Therefore, we also present a set of metrics that may be applied to theoretical
designs, testbeds, and deployed instruments. We show how these quantities may
be used to easily relate the basic properties of the optical instrument to the
detection significance of the given point source in the presence of realistic
noise.Comment: To appear in Proceedings of the SPIE, vol. 1069
Automated reduction of submillimetre single-dish heterodyne data from the James Clerk Maxwell Telescope using ORAC-DR
With the advent of modern multi-detector heterodyne instruments that can
result in observations generating thousands of spectra per minute it is no
longer feasible to reduce these data as individual spectra. We describe the
automated data reduction procedure used to generate baselined data cubes from
heterodyne data obtained at the James Clerk Maxwell Telescope. The system can
automatically detect baseline regions in spectra and automatically determine
regridding parameters, all without input from a user. Additionally it can
detect and remove spectra suffering from transient interference effects or
anomalous baselines. The pipeline is written as a set of recipes using the
ORAC-DR pipeline environment with the algorithmic code using Starlink software
packages and infrastructure. The algorithms presented here can be applied to
other heterodyne array instruments and have been applied to data from
historical JCMT heterodyne instrumentation.Comment: 18 pages, 13 figures, submitted to Monthly Notices of the Royal
Astronomical Societ
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