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The use and application of multisensor data sets are an absolute necessity for remote sensing to fulfill its promise. Initial efforts at registering dissimilar images have been made, but results are not widely available. This paper describes some initial work to better understand the difficulties of multisensor registration. Accuracies achieved are in excess of a pixel diameter.
Issues associated with the registration and rectification of remotely sensed data. Near and long range applications research tasks and some medium range technology augmentation research areas are recommended. Image sharpness, feature extraction, inter-image mapping, error analysis, and verification methods are addressed.
The application of solid-state detector array imaging to the remote sensing of the earth as a follow-on to currently planned Landsat missions provides the opportunity to achieve significantly increased performance. First-order advantages to be anticipated are higher sensitivity resulting in greater radiometric accuracies and improved geometric fidelity. The Multispectral Mapper design concept is responsive to a broad range of user needs by incorporating in-flight selection of spectral bands, instantaneous fields of view, and swath width. This versatility is achieved by the use of an imaging spectrometer which permits both spatial and spectral sampling in the image plane using area array detectors. Other improvements over the current Landsat multispectral scanner and Thematic Mapper include higher spatial resolution and inherently precise registration of the spectral bands. The key technologies required in order to realize these improvements include short-wavelength infrared detectors, wide field of view, broad spectral coverage optics, focal plane cooling, and high-speed onboard signal processing. Significant development activities will be required if an advanced remote sensing capability is to be implemented.
Proposed rectangular array of photodetectors in imaging spectrometer includes integral filter deposited directly on imaging array which would not become misaligned or misregistered as result of mechanical shock, vibration, or differential thermal expansion. Need for precise alignment and registration of filter cannot be over-emphasized.
The Arcsecond Space Telescope Enabling Research in Astrophysics (ASTERIA) was a 6-unit CubeSat technology demonstration mission that was built at NASA’s Jet Propulsion Laboratory (JPL) and deployed from the International Space Station (ISS) on November 20th, 2017. After successfully completing its 90-day primary mission that demonstrated arcsecond-level line-of-sight pointing and focal plane thermal stability for exoplanet detection, it entered an extended mission performing onboard software demonstrations alongside science until end of mission in December 2019. At the end of its lifetime it was being used as a demonstration platform for several experiments, including low earth orbit (LEO) optical navigation operations. With its visible light astrometric camera and stable attitude control system, the ASTERIA spacecraft showed itself to be a capable platform for the imaging of geosynchronous satellites from LEO. This paper will describe the imagery attained in flight and also the image processing algorithms that were developed to render that imagery into navigation quality data. These algorithms dealt with hot pixel filtering, noise modeling, attitude registration, star signal rejection and satellite signal identification. Brightness prediction algorithms used for target selection will also be discussed.
A document describes cryogenic test architecture for the James Webb Space Telescope (JWST) integrated science instrument module (ISIM). The ISIM element primarily consists of a mechanical metering structure, three science instruments, and a fine guidance sensor. One of the critical optomechanical alignments is the co-registration of the optical telescope element (OTE) exit pupil with the entrance pupils of the ISIM instruments. The test architecture has been developed to verify that the ISIM element will be properly aligned with the nominal OTE exit pupil when the two elements come together. The architecture measures three of the most critical pupil degrees-of-freedom during optical testing of the ISIM element. The pupil measurement scheme makes use of specularly reflective pupil alignment references located inside the JWST instruments, ground support equipment that contains a pupil imaging module, an OTE simulator, and pupil viewing channels in two of the JWST flight instruments. Pupil alignment references (PARs) are introduced into the instrument, and their reflections are checked using the instrument's mirrors. After the pupil imaging module (PIM) captures a reflected PAR image, the image will be analyzed to determine the relative alignment offset. The instrument pupil alignment preferences are specularly reflective mirrors with non-reflective fiducials, which makes the test architecture feasible. The instrument channels have fairly large fields of view, allowing PAR tip/tilt tolerances on the order of 0.5deg.
The paper discusses integration of Landsat, Seasat, and other geographic information sources. Mosaicking of radar data and registration of radar to Landsat digital imagery are described, and six types of geophysical data, including gravity and magnetic measurements, are integrated and analyzed using image processing techniques.
Standard LANDSAT-4 MSS digital image data were analyzed for geometric accuracy using two P-format (UTM projection) images of the Washington, D.C. area, scene day 109 (ID number 4010915140) and scene day 125 (ID number 4012515144). Both scenes were tested for geodetic registration accuracy (scene-to-map), temporal registration accuracy (scene-to-scene), and band-to-band registration accuracy (within a scene). The combined RMS error for geodetic registration accuracy was 0.43 pixel (25.51 meters), well within specifications. The comparison between the 2 scenes was made on a band-by-band basis. The 90 percent error figure for temporal registration was 0.68 (57x57) pixel (38.8 meters). Although this figure is larger than the specification, it can be considered excellent with respect to user application. The best case registration errors between bands 1 and 2, and 3 and 4 were 14.2m and 13.7m, respectively, both within specifications. The worst case registration error was 38.0 m between bands 2 and 3.
A novel multiple-camera system for the recording of digital particle image velocimetry (DPIV) images acquired in a two-dimensional separating/reattaching flow is described. The measurements were performed in the NASA Langley Subsonic Basic Research Tunnel as part of an overall series of experiments involving the simultaneous acquisition of dynamic surface pressures and off-body velocities. The DPIV system utilized two frequency-doubled Nd:YAG lasers to generate two coplanar, orthogonally polarized light sheets directed upstream along the horizontal centerline of the test model. A recording system containing two pairs of matched high resolution, 8-bit cameras was used to separate and capture images of illuminated tracer particles embedded in the flow field. Background image subtraction was used to reduce undesirable flare light emanating from the surface of the model, and custom pixel alignment algorithms were employed to provide accurate registration among the various cameras. Spatial cross correlation analysis with median filter validation was used to determine the instantaneous velocity structure in the separating/reattaching flow region illuminated by the laser light sheets. In operation the DPIV system exhibited a good ability to resolve large-scale separated flow structures with acceptable accuracy over the extended field of view of the cameras. The recording system design provided enhanced performance versus traditional DPIV systems by allowing a variety of standard and non-standard cameras to be easily incorporated into the system.
Although much has been learned about the functional organization of the human brain through lesion-deficit analysis, the variety of statistical and image-processing methods developed for this purpose precludes a closed-form analysis of the statistical power of these systems. Therefore, we developed a lesion-deficit simulator (LDS), which generates artificial subjects, each of which consists of a set of functional deficits, and a brain image with lesions; the deficits and lesions conform to predefined distributions. We used probability distributions to model the number, sizes, and spatial distribution of lesions, to model the structure-function associations, and to model registration error. We used the LDS to evaluate, as examples, the effects of the complexities and strengths of lesion-deficit associations, and of registration error, on the power of lesion-deficit analysis. We measured the numbers of recovered associations from these simulated data, as a function of the number of subjects analyzed, the strengths and number of associations in the statistical model, the number of structures associated with a particular function, and the prior probabilities of structures being abnormal. The number of subjects required to recover the simulated lesion-deficit associations was found to have an inverse relationship to the strength of associations, and to the smallest probability in the structure-function model. The number of structures associated with a particular function (i.e., the complexity of associations) had a much greater effect on the performance of the analysis method than did the total number of associations. We also found that registration error of 5 mm or less reduces the number of associations discovered by approximately 13% compared to perfect registration. The LDS provides a flexible framework for evaluating many aspects of lesion-deficit analysis.
Scene radiation and atmospheric effects, mathematical pattern recognition and image analysis, information evaluation and utilization, and electromagnetic measurements and signal handling are considered. Research issues in sensors and signals, including radar (SAR) reflectometry, SAR processing speed, registration, including overlay of SAR and optical imagery, entire system radiance calibration, and lack of requirements for both sensors and systems, etc. were discussed.
Postprocessing system for synthetic-aperture radar (SAR) transforms raw images from natural rotated and distorted SAR reference frame into geocoded images. Images automatically corrected to remove slant-range nonlinearities and Doppler skew. Produces multiple-frame mosaics for large-scale mapping. Does not require tedious manual registration of representative "tie" points in raw SAR imagery with known locations on Earth.
Geometric and probabilistic models for subpixel accuracy are developed. The geometric models bound the error in offset estimation using the pixels in an observed digital straight line. One probabilistic model bounds the estimate of error offset for continuous images. The other model bounds the error for discrete images given that one is in the correct pixel.
Research using satellite remotely sensed data, even within any single scientific discipline, often lacked a unifying principle or strategy with which to plan or integrate studies conducted over an area so large that exhaustive examination is infeasible, e.g., the U.S.A. However, such a series of studies would seem to be at the heart of what makes satellite remote sensing unique, that is the ability to select for study from among remotely sensed data sets distributed widely over the U.S., over time, where the resources do not exist to examine all of them. Using this philosophical underpinning and the concept of a unifying principle, an operational procedure for developing a sampling strategy and formal testable hypotheses was constructed. The procedure is applicable across disciplines, when the investigator restates the research question in symbolic form, i.e., quantifies it. The procedure is set within the statistical framework of general linear models. The dependent variable is any arbitrary function of remotely sensed data and the independent variables are values or levels of factors which represent regional climatic conditions and/or properties of the Earth's surface. These factors are operationally defined as maps from the U.S. National Atlas (U.S.G.S., 1970). Eighty-five maps from the National Atlas, representing climatic and surface attributes, were automated by point counting at an effective resolution of one observation every 17.6 km (11 miles) yielding 22,505 observations per map. The maps were registered to one another in a two step procedure producing a coarse, then fine scale registration. After registration, the maps were iteratively checked for errors using manual and automated procedures. The error free maps were annotated with identification and legend information and then stored as card images, one map to a file. A sampling design will be accomplished through a regionalization analysis of the National Atlas data base (presently being conducted). From this analysis a map of homogeneous regions of the U.S.A. will be created and samples (LANDSAT scenes) assigned by region.
Abnormal tau inclusions are hallmarks of Alzheimer's disease and predictors of clinical decline. Several tau PET tracers are available for neurodegenerative disease research, opening avenues for molecular diagnosis in vivo. However, few have been approved for clinical use. Understanding the neurobiological basis of PET signal validation remains problematic because it requires a large-scale, voxel-to-voxel correlation between PET and (immuno) histological signals. Large dimensionality of whole human brains, tissue deformation impacting co-registration, and computing requirements to process terabytes of information preclude proper validation. We developed a computational pipeline to identify and segment particles of interest in billion-pixel digital pathology images to generate quantitative, 3D density maps. The proposed convolutional neural network for immunohistochemistry samples, IHCNet, is at the pipeline's core. We have successfully processed and immunostained over 500 slides from two whole human brains with three phospho-tau antibodies (AT100, AT8, and MC1), spanning several terabytes of images. Our artificial neural network estimated tau inclusion from brain images, which performs with ROC AUC of 0.87, 0.85, and 0.91 for AT100, AT8, and MC1, respectively. Introspection studies further assessed the ability of our trained model to learn tau-related features. We present an end-to-end pipeline to create terabytes-large 3D tau inclusion density maps co-registered to MRI as a means to facilitate validation of PET tracers.
Purpose: We evaluated the impact of partial volume correction (PVC) methods on the quantification of longitudinal [ 18 F]GTP1 tau positron-emission tomography (PET) in Alzheimer's disease and the suitability of describing the tau pathology burden temporal trajectories using linear mixed-effects models (LMEM). Methods: We applied van Cittert iterative deconvolution (VC), 2-compartment, and 3-compartment, and the geometric transfer matrix plus region-based voxelwise methods to data acquired in an Alzheimer's disease natural history study over 18 months at a single imaging site. We determined the optimal PVC method by comparing the standardized uptake value ratio change (%ΔSUVR) between diagnostic and tau burden–level groups and the longitudinal repeatability derived from the LMEM. The performance of LMEM analysis for calculating %ΔSUVR was evaluated in a natural history study and in a multisite clinical trial of semorinemab in prodromal to mild Alzheimer's disease by comparing results to traditional per-visit estimates. Results: The VC, 2-compartment, and 3-compartment PVC methods had similar performance, whereas region-based voxelwise overcorrected regions with a higher tau burden. The lowest within-subject variability and acceptable group separation scores were observed without PVC. The LMEM-derived %ΔSUVR values were similar to the per-visit estimates with lower variability. Conclusion: The results indicate that the tested PVC methods do not offer a clear advantage or improvement over non-PVC images for the quantification of longitudinal [ 18 F]GTP1 PET data. LMEM offers a robust framework for the longitudinal tau PET quantification with low longitudinal test–retest variability. Clinical trial registration: NCT02640092 and NCT03289143.
Radiometric calibration accuracy of TM data, radiometric comparison of A-, B-, and P-format data, and geometric registration accuracy of the TM data at enlarged scales were analyzed. Radiometric analysis of the Wind River Basin, Wyoming scene demonstrates that the TM system can be used to extract image reflectance spectra from ground targets following calibration of the system. It was also demonstrated that: (1) image DN (radiance values) vs. ground reflectance calibration scatterplots yield parameters which can be used to constrain atmospheric models and can determine TM radiometric sensitivity; (2) no significant degradation occurs as a result of radiometric and geometric correction by SCROUNGE processing; and (3) TM data can be enlarged to 1:24000 with no major geometric distortions or misregistration problems to USGS topographic maps.
Two conventional nondestructive evaluation techniques were used to evaluate advanced ceramic composite materials. It was shown that neither ultrasonic C-scan nor radiographic imaging can individually provide sufficient data for an accurate nondestructive evaluation. Both ultrasonic C-scan and conventional radiographic imaging are required for preliminary evaluation of these complex systems. The material variations that were identified by these two techniques are porosity, delaminations, bond quality between laminae, fiber alignment, fiber registration, fiber parallelism, and processing density flaws. The degree of bonding between fiber and matrix cannot be determined by either of these methods. An alternative ultrasonic technique, angular power spectrum scanning (APSS) is recommended for quantification of this interfacial bond.