451,806 research outputs found
Spatial images from temporal data
Traditional paradigms for imaging rely on the use of a spatial structure,
either in the detector (pixels arrays) or in the illumination (patterned
light). Removal of the spatial structure in the detector or illumination, i.e.,
imaging with just a single-point sensor, would require solving a very strongly
ill-posed inverse retrieval problem that to date has not been solved. Here, we
demonstrate a data-driven approach in which full 3D information is obtained
with just a single-point, single-photon avalanche diode that records the
arrival time of photons reflected from a scene that is illuminated with short
pulses of light. Imaging with single-point time-of-flight (temporal) data opens
new routes in terms of speed, size, and functionality. As an example, we show
how the training based on an optical time-of-flight camera enables a compact
radio-frequency impulse radio detection and ranging transceiver to provide 3D
images.Comment: This is the final version as published in Optica Vol. 7, Issue 8, pp.
900-905 (2020
Accelerated Dynamic Magnetic Resonance Imaging from Spatial-Subspace Reconstructions (SPARS)
Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) ideally
requires a high spatial and high temporal resolution, but hardware limitations
prevent acquisitions from simultaneously achieving both. Existing image
reconstruction techniques can artificially create spatial resolution at a given
temporal resolution by estimating data that is not acquired, but, ultimately,
spatial details are sacrificed at very high acceleration rates. The purpose of
this paper is to introduce the concept of spatial subspace reconstructions
(SPARS) and demonstrate its ability to reconstruct high spatial resolution
dynamic images from as few as one acquired radial spoke per dynamic frame.
Briefly, a low-temporal-high-spatial resolution organization of the acquired
raw data is used to estimate a spatial subspace in which the
high-temporal-high-spatial ground truth data resides. This subspace is then
used to estimate entire images from single k-space spokes. In both simulated
and human in-vivo data, the proposed SPARS reconstruction method outperformed
standard GRASP and GRASP-Pro reconstruction, providing a shorter reconstruction
time and yielding higher accuracy from both a spatial and temporal perspective.Comment: Submitted to Frontiers in Imagin
Achieving Consistent Doppler Measurements from SDO/HMI Vector Field Inversions
NASA's Solar Dynamics Observatory is delivering vector field observations of
the full solar disk with unprecedented temporal and spatial resolution;
however, the satellite is in a highly inclined geostationary orbit. The
relative spacecraft-Sun velocity varies by ~km/s over a day which
introduces major orbital artifacts in the Helioseismic Magnetic Imager data. We
demonstrate that the orbital artifacts contaminate all spatial and temporal
scales in the data. We describe a newly-developed three stage procedure for
mitigating these artifacts in the Doppler data derived from the Milne-Eddington
inversions in the HMI Pipeline. This procedure was applied to full disk images
of AR11084 to produce consistent Dopplergrams. The data adjustments reduce the
power in the orbital artifacts by 31dB. Furthermore, we analyze in detail the
corrected images and show that our procedure greatly improve the temporal and
spectral properties of the data without adding any new artifacts. We conclude
that this new and easily implemented procedure makes a dramatic improvement in
the consistency of the HMI data and in its usefulness for precision scientific
studies.Comment: 58 pages, 19 figures, submitted to Ap
A new class of multiscale lattice cell (MLC) models for spatio-temporal evolutionary image representation
Spatio-temporal evolutionary (STE) images are a class of complex dynamical systems that evolve over both space and time. With increased interest in the investigation of nonlinear complex phenomena, especially spatio-temporal behaviour governed by evolutionary laws that are dependent
on both spatial and temporal dimensions, there has been an increased need to investigate model identification methods for this class of complex systems. Compared with pure temporal processes, the identification of spatio-temporal models from observed images is much more difficult and quite
challenging. Starting with an assumption that there is no apriori information about the true model but
only observed data are available, this study introduces a new class of multiscale lattice cell (MLC)
models to represent the rules of the associated spatio-temporal evolutionary system. An application to a chemical reaction exhibiting a spatio-temporal evolutionary behaviour, is investigated to demonstrate the new modelling framework
Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps
Studies of land cover dynamics would benefit greatly from the generation of land cover maps at both fine spatial and temporal resolutions. Fine spatial resolution images are usually acquired relatively infrequently, whereas coarse spatial resolution images may be acquired with a high repetition rate but may not capture the spatial detail of the land cover mosaic of the region of interest. Traditional image spatial–temporal fusion methods focus on the blending of pixel spectra reflectance values and do not directly provide land cover maps or information on land cover dynamics. In this research, a novel Spatial–Temporal remotely sensed Images and land cover Maps Fusion Model (STIMFM) is proposed to produce land cover maps at both fine spatial and temporal resolutions using a series of coarse spatial resolution images together with a few fine spatial resolution land cover maps that pre- and post-date the series of coarse spatial resolution images. STIMFM integrates both the spatial and temporal dependences of fine spatial resolution pixels and outputs a series of fine spatial–temporal resolution land cover maps instead of reflectance images, which can be used directly for studies of land cover dynamics. Here, three experiments based on simulated and real remotely sensed images were undertaken to evaluate the STIMFM for studies of land cover change. These experiments included comparative assessment of methods based on single-date image such as the super-resolution approaches (e.g., pixel swapping-based super-resolution mapping) and the state-of-the-art spatial–temporal fusion approach that used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Flexible Spatiotemporal DAta Fusion model (FSDAF) to predict the fine-resolution images, in which the maximum likelihood classifier and the automated land cover updating approach based on integrated change detection and classification method were then applied to generate the fine-resolution land cover maps. Results show that the methods based on single-date image failed to predict the pixels of changed and unchanged land cover with high accuracy. The land cover maps that were obtained by classification of the reflectance images outputted from ESTARFM and FSDAF contained substantial misclassification, and the classification accuracy was lower for pixels of changed land cover than for pixels of unchanged land cover. In addition, STIMFM predicted fine spatial–temporal resolution land cover maps from a series of Landsat images and a few Google Earth images, to which ESTARFM and FSDAF that require correlation in reflectance bands in coarse and fine images cannot be applied. Notably, STIMFM generated higher accuracy for pixels of both changed and unchanged land cover in comparison with other methods
村落レベルでの生業活動追跡のための時空間データ収集
We aim to use a multi-spatial and temporal approach to trace people’s livelihoods from a village to a regional level. For this, we accumulated various spatial data and considered the seasonal and inter-annual changes. The principal data was from satellite images, aerial photographs and a crop allocation map determined by field investigation. Our concept of a multi-spatial and temporal approach was used to integrate the various kinds of data
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