Contemporaneous spectral imaging of Jupiter by Galileo/NIMS and Cassini/VIMS: preliminary science results
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Abstract We present an approach that combines photon spectrum correlation analysis with the reconstruction of three-dimensional momentum distribution from velocity map images in an efficient, single-step procedure. We demonstrate its efficacy with the results from the photoionization of the 2 p -shell of argon using the Free-electron LASer in Hamburg free-electron laser (FEL). Distinct spectral features due to the spin-orbit splitting of Ar + ( 2 p − 1 ) are resolved, despite the large average bandwidth of the ionizing pulses from the FEL. This demonstrates a clear advantage over the conventional analysis method, and it will be broadly beneficial for velocity map imaging experiments with FEL sources. The retrieved linewidth of the binding energy spectrum approaches the resolution limitation prescribed by the spectrometers used to collect the data. Our approach presents a path to extend spectral-domain ghost imaging to the case where the photoproduct observable is high-dimensional.
The Infrared Spectral Imaging Radiometer experiment was flown on a space shuttle mission as a shuttle hitchhiker experiment in August of 1997. The goals of the experiment were to test uncooled array detectors for infrared spectral imaging from space and to apply for the first time retrieval from space of brightness temperatures of cloud, land and sea along with direct laser measurements of cloud top height. The instrument operates in 3 narrow and one broad spectral band, all between 7 and 13 microns in either stare or time-delay and integration mode. The nominal spatial resolution was 1/4 kilometer. Using onboard calibrations along with periodic views of deep space, radiometric calibration of imagery was carried out and performance analyzed. The noise equivalent temperature difference and absolute accuracy reported here varied with operating mode, spectral band and scene temperature but were within requirements. This paper provides a description of the instrument, its operating modes, the method of brightness temperature retrieval, the method of spectral registration and results from the flight.
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As spectral imaging techniques are becoming more prominent in science, advanced image segmentation algorithms are required to identify appropriate domains in these images. We present a version of image segmentation called manifold projection image segmentation (MPIS) that is generally applicable to a broad range of systems without the need for training because MPIS uses unsupervised machine learning with a few physically motivated hyperparameters. We apply MPIS to nanoscale x-ray absorption near edge structure (XANES) imaging, where XANES spectra are collected with nanometer spatial resolution. We show the superiority of manifold projection over linear transformations, such as the commonly used principal component analysis (PCA). Moreover, MPIS maintains accuracy while reducing computation time and sensitivity to noise compared to the standard nano-XANES imaging analysis procedure. Finally, we demonstrate how multimodal information, such as x-ray fluorescence data and spatial location of pixels, can be incorporated into the MPIS framework. We propose that MPIS is adaptable for any spectral imaging technique, including scanning transmission x-ray microscopy, where the length scale of domains is larger than the resolution of the experiment.
Here, a multimodal imaging study of chabazite is used to show the distribution of and discriminate between different emissive deposits arising as a result of the detemplation process. 3D confocal imaging, fluorescence lifetime imaging, spectral imaging, and Raman mapping are used to show three different types of emissive behaviours each characterised by different spatial distributions, trends in lifetime, spectral signals, and Raman signatures. A notable difference is seen in the morphology of agglomerated surface deposits and larger subsurface deposits, which experience lifetime augmentation due to spatial confinement. The distribution of organic residue throughout the crystal volume is comparable to XRF mapping that shows Si enrichment on the outer edges and higher Al content through the centre, demonstrating that a fluorescence-based technique can also be used to indirectly comment on the compositional chemistry of the inorganic framework.
An algorithm and a computer program that implements the algorithm that performs recursive hierarchical segmentation (RHSEG) of data have been developed. While the current implementation is for two-dimensional data having spatial characteristics (e.g., image, spectral, or spectral-image data), the generalized algorithm also applies to three-dimensional or higher dimensional data and also to data with no spatial characteristics. The algorithm and software are modified versions of a prior RHSEG algorithm and software, the outputs of which often contain processing-window artifacts including, for example, spurious segmentation-image regions along the boundaries of processing-window edges.
Long-wavelength infrared (LWIR) focal plane arrays (FPAs) needed for Earth Science imaging, spectral imaging, and sounding applications have always been among the most challenging in infrared photodetector technology due to the rigorous material growth, device design and fabrication demands. Future small satellite missions will present even more challenges for LWIR FPAs, as operating temperature must be increased so that cooler (and radiator) volume, mass, and power can be reduced.
Spectral images of high quality obtained. Acousto-optical-tunable-filter (AOTF) imaging spectrometer is optical system in which AOTF serves as spectrally dispersive element causing image on final focal plane to be shifted on plane by distance depending on wavelength of light emanating from scene. Useful in several applications involving identification, via characteristic spectras, of substances in observed scenes: examples include prospecting for minerals and detecting chemical pollutants.
Improved scanners to be incorporated into hyperspectral microscope-based imaging systems have been invented. Heretofore, in microscopic imaging, including spectral imaging, it has been customary to either move the specimen relative to the optical assembly that includes the microscope or else move the entire assembly relative to the specimen. It becomes extremely difficult to control such scanning when submicron translation increments are required, because the high magnification of the microscope enlarges all movements in the specimen image on the focal plane. To overcome this difficulty, in a system based on this invention, no attempt would be made to move either the specimen or the optical assembly. Instead, an objective lens would be moved within the assembly so as to cause translation of the image at the focal plane: the effect would be equivalent to scanning in the focal plane. The upper part of the figure depicts a generic proposed microscope-based hyperspectral imaging system incorporating the invention. The optical assembly of this system would include an objective lens (normally, a microscope objective lens) and a charge-coupled-device (CCD) camera. The objective lens would be mounted on a servomotor-driven translation stage, which would be capable of moving the lens in precisely controlled increments, relative to the camera, parallel to the focal-plane scan axis. The output of the CCD camera would be digitized and fed to a frame grabber in a computer. The computer would store the frame-grabber output for subsequent viewing and/or processing of images. The computer would contain a position-control interface board, through which it would control the servomotor. There are several versions of the invention. An essential feature common to all versions is that the stationary optical subassembly containing the camera would also contain a spatial window, at the focal plane of the objective lens, that would pass only a selected portion of the image. In one version, the window would be a slit, the CCD would contain a one-dimensional array of pixels, and the objective lens would be moved along an axis perpendicular to the slit to spatially scan the image of the specimen in pushbroom fashion. The image built up by scanning in this case would be an ordinary (non-spectral) image. In another version, the optics of which are depicted in the lower part of the figure, the spatial window would be a slit, the CCD would contain a two-dimensional array of pixels, the slit image would be refocused onto the CCD by a relay-lens pair consisting of a collimating and a focusing lens, and a prism-gratingprism optical spectrometer would be placed between the collimating and focusing lenses. Consequently, the image on the CCD would be spatially resolved along the slit axis and spectrally resolved along the axis perpendicular to the slit. As in the first-mentioned version, the objective lens would be moved along an axis perpendicular to the slit to spatially scan the image of the specimen in pushbroom fashion.
We are developing science analyses algorithms to interface with a Geologist's Field Assistant device to allow robotic or human remote explorers to better sense their surroundings during limited surface excursions. Our algorithms will interpret spectral and imaging data obtained by various sensors. Additional information is contained in the original extended abstract.
High-spectral resolution images (AVIRIS) of the cuprite mining area were used to evaluate atmospheric calibration algorithms and test several mineral mapping techniques. Four scene normalization techniques were used: (1) the flat-field method, (2) the internal average reflectance method, (3) the empirical line method, and (4) the atmospheric absorption removal method (ATREM). The algorithms were evaluated in terms of their spectral interpret- ability and their ability to remove both solar irradiance and atmospheric absorption features, noise, and artifacts. Noise was quantified by calculating the coefficient of variation of the spectra, and spectral interpretability was quantified by calcu- lating a difference spectrum (eg, laboratory spectrum minus pixel spectrum) for areas with known occurrences of clay minerals. These difference spectra were useful in evaluating the degree of removal of atmospheric features. The empirical line method produced the best calibration results. Mineral mapping as done using (1) color-composites of bands on the shoulders and centers of expected absorption features, (2) color-coded spectra, and (3) spectral angle mapping.
We present a neural network based multi-‐spectral image segmentation method. A neural network is trained on the selected features of both the objects and background in the longwave (LW) Infrared (IR) images. Multiple iterations of training are performed until the accuracy of the segmentation reaches satisfactory level. The segmentation boundary of the LW image is used to segment the midwave (MW) and shortwave (SW) IR images. A second neural network detects the local discontinuities and refines the accuracy of the local boundaries. The neural net based segmentation method is compared with Wavelet-‐threshold and Grab-‐Cut methods. Test results have shown increased accuracy and robustness of this segmentation scheme for multi-‐spectral IR images.