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At least 19 records

Nested-hierarchical scene models and image segmentation

An improved model of scenes for image analysis purposes, a nested-hierarchical approach which explicitly acknowledges multiple scales of objects or categories of objects, is presented. A multiple-pass, region-based segmentation algorithm improves the segmentation of images from scenes better modeled as a nested hierarchy. A multiple-pass approach allows slow and careful growth of regions while interregion distances are below a global threshold. Past the global threshold, a minimum region size parameter forces development of regions in areas of high local variance. Maximum and viable region size parameters limit the development of undesirably large regions. Application of the segmentation algorithm for forest stand delineation in TM imagery yields regions corresponding to identifiable features in the landscape. The use of a local variance, adaptive-window texture channel in conjunction with spectral bands improves the ability to define regions corresponding to sparsely stocked forest stands which have high internal variance.

Woodcock, C.↗

The use of variograms in remote sensing. I - Scene models and simulated images. II - Real digital images

Theoretical and empirical studies of variograms are presented. The sensitivity of variograms is studied through varyig parameters of scene models both in calculating explicit variograms and in simulating images. It is found that the heights of variograms are related to the proportion of an area covered by objects. It is shown that the range of influence of a variogram is related to the size of the objects in the scene and that the shape of the variogram becomes more rounded as the variance in the size distribution of objects increases. In the second part, empirically calculated variograms from real digital images are used to demonstrate these theoretical findings. These calculated variograms also show the periodicity in ground scenes and reveal anisotropy.

Woodcock, Curtis E.↗

AgRISTARS. Supporting research: Algorithms for scene modelling

The requirements for a comprehensive analysis of LANDSAT or other visual data scenes are defined. The development of a general model of a scene and a computer algorithm for finding the particular model for a given scene is discussed. The modelling system includes a boundary analysis subsystem, which detects all the boundaries and lines in the image and builds a boundary graph; a continuous variation analysis subsystem, which finds gradual variations not well approximated by a boundary structure; and a miscellaneous features analysis, which includes texture, line parallelism, etc. The noise reduction capabilities of this method and its use in image rectification and registration are discussed.

Rassbach, M. E.↗

On the nature of models in remote sensing

An explicit framework can provide a better understanding of remote sensing models and their interrelationships. This framework distinguishes between the scene, which is real and exists on the ground, and the image, which is a collection of spatially arranged masurements drawn from the scene. The scene model generalizes and parameterizes the essential qualities of the scene. Scene models may be discrete, in which the scene model consists of discrete elements with boundaries, or continuous, in which matter and energy flows are taken to be continuous and there are no clear or sharp boundaries in the scene. In the discrete case, there are two possibilities for models: H- and L-resolution. In the H-resolution case, the resolution cells of the image are smaller than the elements, and thus the elements may be individually resolved. In the L-resolution case, the resolution cells are larger than the elements and cannot be resolved. Most canopy models are L-resolution, deterministic, and noninvertible in nature; image processing models, however, tend to be H-resolution, empirical, and invertible. This taxonomy helps add insight to the development of remote sensing theory and point the way to new, productive areas of research.

Strahler, A. H.↗

Imaging infrared: Scene simulation, modeling, and real image tracking; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

Various papers on scene simulation, modeling, and real image tracking using IR imaging are presented. Individual topics addressed include: tactical IR scene generator, dynamic FLIR simulation in flight training research, high-speed dynamic scene simulation in UV to IR spectra, development of an IR sensor calibration facility, IR celestial background scene description, transmission measurement of optical components at cryogenic temperatures, diffraction model for a point-source generator, silhouette-based tracking for tactical IR systems, use of knowledge in electrooptical trackers, detection and classification of target formations in IR image sequences, SMPRAD: simplified three-dimensional cloud radiance model, IR target generator, recent advances in testing of thermal imagers, generic IR system models with dynamic image generation, modeling realistic target acquisition using IR sensors in multiple-observer scenarios, and novel concept of scene generation and comprehensive dynamic sensor test.

Triplett, Milton J.↗

Improved canopy reflectance modeling and scene inference through improved understanding of scene pattern

The Li-Strahler reflectance model, driven by LANDSAT Thematic Mapper (TM) data, provided regional estimates of tree size and density within 20 percent of sampled values in two bioclimatic zones in West Africa. This model exploits tree geometry in an inversion technique to predict average tree size and density from reflectance data using a few simple parameters measured in the field (spatial pattern, shape, and size distribution of trees) and in the imagery (spectral signatures of scene components). Trees are treated as simply shaped objects, and multispectral reflectance of a pixel is assumed to be related only to the proportions of tree crown, shadow, and understory in the pixel. These, in turn, are a direct function of the number and size of trees, the solar illumination angle, and the spectral signatures of crown, shadow and understory. Given the variance in reflectance from pixel to pixel within a homogeneous area of woodland, caused by the variation in the number and size of trees, the model can be inverted to give estimates of average tree size and density. Because the inversion is sensitive to correct determination of component signatures, predictions are not accurate for small areas.

Franklin, Janet↗

A Multi-Wavelength Thermal Infrared and Reflectance Scene Simulation Model

Several theoretical calculations are presented and our approach discussed for simulating overall composite scene thermal infrared exitance and canopy bidirectional reflectance of a forest canopy. Calculations are performed for selected wavelength bands of the DOE Multispectral Thermal Imagery and comparisons with atmospherically corrected MTI imagery are underway. NASA EO-1 Hyperion observations also are available and the favorable comparison of our reflective model results with these data are reported elsewhere.

Ballard, J. R., Jr.↗

Fundamental remote sensing science research program: The Scene Radiation and Atmospheric Effects Characterization Project

The Scene Radiation and Atmospheric Effects Characterization (SRAEC) Project was established within the NASA Fundamental Remote Sensing Science Research Program to improve our understanding of the fundamental relationships of energy interactions between the sensor and the surface target, including the effect of the atmosphere. The current studies are generalized into the following five subject areas: optical scene modeling, Earth-space radiative transfer, electromagnetic properties of surface materials, microwave scene modeling, and scatterometry studies. This report has been prepared to provide a brief overview of the SRAEC Project history and objectives and to report on the scientific findings and project accomplishments made by the nineteen principal investigators since the project's initiation just over three years ago. This annual summary report derives from the most recent annual principal investigators meeting held January 29 to 31, 1985.

Deering, D. W.↗

Top-of-Atmosphere Albedo Estimation from Angular Distribution Models using Scene Identification from Satellite Cloud Property Retrievals

The next generation of Earth radiation budget satellite instruments will routinely merge estimates of global top-of-atmosphere radiative fluxes with cloud properties. This information will offer many new opportunities for validating radiative transfer models and cloud parameterizations in climate models. In this study, five months of POLarization and Directionality of the Earth's Reflectances (POLDER) 670 nm radiance measurements are considered in order to examine how satellite cloud property retrievals can be used to define empirical Angular Distribution Models (ADMs) for estimating top-of-atmosphere (TOA) albedo. ADMs are defined for 19 scene types defined by satellite retrievals of cloud fraction and cloud optical depth. Two approaches are used to define the ADM scene types: The first assumes there are no biases in the retrieved cloud properties and defines ADMs for fixed discrete intervals of cloud fraction and cloud optical depth (fixed-tau approach). The second approach involves the same cloud fraction intervals, but uses percentile intervals of cloud optical depth instead (percentile-tau approach). Albedos generated using these methods are compared with albedos inferred directly from the mean observed reflectance field. Albedos based on ADMs that assume cloud properties are unbiased (fixed-tau approach) show a strong systematic dependence on viewing geometry. This dependence becomes more pronounced with increasing solar zenith angle, reaching approximately equals 12% (relative) between near-nadir and oblique viewing zenith angles for solar zenith angles between 60 deg and 70 deg. The cause for this bias is shown to be due to biases in the cloud optical depth retrievals. In contrast, albedos based on ADMs built using percentile intervals of cloud optical depth (percentile-tau approach) show very little viewing zenith angle dependence and are in good agreement with albedos obtained by direct integration of the mean observed reflectance field (less than 1% relative error). When the ADMs are applied separately to populations consisting of only liquid water and ice clouds, significant biases in albedo with viewing geometry are observed (particularly at low sun elevations), highlighting the need to account for cloud phase both in cloud optical depth retrievals and in defining ADM scene types. ADM-derived monthly mean albedos determined for all 5 deg x 5 deg latitude/longitude regions over ocean are in good agreement (regional RMS relative errors less than 2%) with those obtained by direct integration when ADM albedos inferred from specific angular bins are averaged together. Albedos inferred from near-nadir and oblique viewing zenith angles are the least accurate, with regional RMS errors reaching approximately 5-10% (relative). Compared to an earlier study involving ERBE ADMs, regional mean albedos based on the 19 scene types considered here show a factor of 4 reduction in bias error and a factor of 3 reduction in RMS error.

Loeb, N. G.↗

Discrete-object modeling of remotely sensed scenes

Remotely sensed scenes can be modeled as collections of discrete, three-dimensional objects that cast shadows on a background. Approaching scenes from this perspective has led to two related lines of research. First is the geometric/optical modeling of a forest canopy, in which conifers are modeled as cones whose size and spacing vary according to functions established by field measurements. This canopy model is 'L-resolution' in nature - the objects are smaller than the resolution cells of the image and cannot be resolved individually. Second is scene modeling in which image variance is taken as a function of the relationship between the size, shape and spacing of objects and the resolution cell size of the digital image derived from the scene. This modeling is 'H-resolution' in nature - the objects in the scene are assumed to be larger than the resolution cells of the image, and thus can be individually distinguished. Both approaches illustrate the utility of the discrete-object scene model in extracting information from remotely sensed scenes.

Strahler, A. H.↗

Autocorrelation and regularization in digital images. II - Simple image models

The variogram function used in geostatistical analysis is a useful statistic in the analysis of remotely sensed images. Using the results derived by Jupp et al. (1988), the basic second-order, or covariance, properties of scenes modeled by simple disks of varying size and spacing after imaging into disk-shaped pixels are analyzed to explore the relationship betwee image variograms and discrete object scene structure. The models provide insight into the nature of real images of the earth's surface and the tools for a complete analysis of the more complex case of three-dimensional illuminated discrete-object images.

Jupp, David L. B.↗

Evaluation of Spectral Band Adjustment Factors for Cross-Calibration of Visible Imagers

The CERES EBAF dataset provides TOA SW and LW fluxes for long-term monitoring of the Earth’s energy balance and to validate climate models. The EBAF products, based on the Terra and Aqua CERES instrument observed radiances, rely on coincident measurements from the onboard MODIS imagers to determine cloud properties that are used in the angular distribution model scene selection required to convert the CERES observed radiances into flux values. Once the Terra and Aqua orbits start drifting outside of their 15-minute window, the CERES project will rely solely on the NOAA-20 CERES observations. A seamless transition of fluxes and clouds can only occur if the analogous MODIS and VIIRS channels are properly inter-calibrated. The visible band (0.65 µm) in MODIS (band 1) and VIIRS (M5 or I1) is critical for retrieving the cloud mask and optical depth. The spectral response functions (SRF) of these bands differ noticeably and will require scene dependent spectral band adjustment factors (SBAF) for proper radiometric scaling between them. Fortunately, the VIIRS I1 and M5 bands are calibrated using the same solar diffuser as a reference, which implies that band reflectance differences must be due to the spectral disparity. The coincident I1 and M5 TOA reflectance measurements provide the optimal opportunity to validate SBAFs over many surface and cloud conditions. The CERES project maintains SCIAMACHY, GOME-2, Hyperion, and radiative transfer model-based scene-stratified hyper-spectral reflectance measurements that can be convolved with sensor pair SRFs to compute the corresponding SBAF. This study will evaluate the SCIAMACHY, GOME-2, Hyperion, and RTM based SBAFs over tropical ocean targets used in SNO inter-calibration of MODIS and VIIRS, including clear-sky ocean, deep convective clouds (DCC), and liquid cloud targets in terms of their applicability to absolute intercomparison of visible imagers. The large SCIAMACHY and GOME-2 footprints and the lack of Hyperion cloudy scenes will impact the resulting SBAFs and necessitates the need for future CLARREO hyper-spectral reflectances to decrease SBAF uncertainties Preliminary results indicate that the SCIAMACHY and GOME-2 based SBAFs for the VIIRS I1 and M5 band pairs may differ by 1.5% for some ATO scene types. Because most of the modern GEOs visible band SRFs encompass the VIIRS I1 band SRF, these evaluations are critical to ensure that the MODIS, VIIRS, and GEO clouds and fluxes are consistent. SBAFs for Libya-4, Dome-C and other Earth invariant target will also be evaluated.

D. R. Doelling↗

Enhanced Graphics for Extended Scale Range

Enhanced Graphics for Extended Scale Range is a computer program for rendering fly-through views of scene models that include visible objects differing in size by large orders of magnitude. An example would be a scene showing a person in a park at night with the moon, stars, and galaxies in the background sky. Prior graphical computer programs exhibit arithmetic and other anomalies when rendering scenes containing objects that differ enormously in scale and distance from the viewer. The present program dynamically repartitions distance scales of objects in a scene during rendering to eliminate almost all such anomalies in a way compatible with implementation in other software and in hardware accelerators. By assigning depth ranges correspond ing to rendering precision requirements, either automatically or under program control, this program spaces out object scales to match the precision requirements of the rendering arithmetic. This action includes an intelligent partition of the depth buffer ranges to avoid known anomalies from this source. The program is written in C++, using OpenGL, GLUT, and GLUI standard libraries, and nVidia GEForce Vertex Shader extensions. The program has been shown to work on several computers running UNIX and Windows operating systems.

Hanson, Andrew J.↗

A Model of Manual Control with Perspective Scene Viewing

A model of manual control during perspective scene viewing is presented, which combines the Crossover Model with a simpli ed model of perspective-scene viewing and visual- cue selection. The model is developed for a particular example task: an idealized constant- altitude task in which the operator controls longitudinal position in the presence of both longitudinal and pitch disturbances. An experiment is performed to develop and vali- date the model. The model corresponds closely with the experimental measurements, and identi ed model parameters are highly consistent with the visual cues available in the perspective scene. The modeling results indicate that operators used one visual cue for position control, and another visual cue for velocity control (lead generation). Additionally, operators responded more quickly to rotation (pitch) than translation (longitudinal).

pilot modeling↗

Analytical design of multispectral sensors

An optimal design based on the criterion of minimum mean square representation error using the Karhunen-Loeve expansion was developed to represent the spectral response functions from a stratum based upon a stochastic process scene model. From the overall pattern recognition system perspective, the effect of the representation accuracy on a typical performance criterion (the probability of correct classification) is investigated. The optimum sensor design provides a standard against which practical (suboptimum) operational sensors can be compared. An example design is provided and its performance is illustrated. Although developed primarily for the purpose of sensor design, the procedure has potential for making important contributions to scene understanding. Spectral channels which have narrow bandwidths relative to current sensor systems may be necessary to provide adequate spectral representation and improved classification performance.

Wiersma, D. J.↗

A Modeling and Simulation Study for the GeoXO Atmospheric Composition Instrument (ACX): System Level SO2 and O3 Retrieval Performance as a Function of SNR

NOAA’s Geostationary Extended Observations (GeoXO) program is planning to include a hyperspectral UV/visible atmospheric composition instrument in geostationary orbit slated for operations by the early 2030s. Trace gas retrievals provide critical information about our Earth’s system as it relates to both natural and anthropogenic activity. This work focused on the impacts of the signal-to-noise ratio (SNR) on the retrieval uncertainty of SO2 and O3. An end-to-end physics-based imaging system and scene modeling and simulation framework was developed to assess instrument performance considerations in support of the planned GeoXO atmospheric composition instrument (ACX). The framework is composed of three primary components: 1) simulated at-sensor radiance via radiative transfer models of a known atmospheric composition, 2) a noise model driven by previous and planned sensor specifications, and 3) a physical retrieval algorithm. In this analysis, the tropospheric concentrations of SO2 and O3 in the atmospheric column were varied in the boundary layer (0-3 km) and at an elevation of 5 km with a thickness of 1-2 km; the uncertainty in retrieval was studied for various SNR levels for multiple viewing geometries and solar zenith angles. This effort supports the development of the GeoXO ACX instrument by providing a simulated ACX performance assessment of the system-level retrieval performance as a function of SNR, particularly with respect to the wavelength range of interest (approximately 305 – 340 nm) for SO2 and O3 trace gas retrievals.

Monica Cook↗

The Dark Energy Survey Supernova Program: Light Curves and 5 Yr Data Release

We present griz photometric light curves for the full 5 yr of the Dark Energy Survey Supernova (DES-SN) program, obtained with both forced point-spread function photometry on difference images (DiffImg) performed during survey operations, and scene modelling photometry (SMP) on search images processed after the survey. This release contains 31,636 DiffImg and 19,706 high-quality SMP light curves, the latter of which contain 1635 photometrically classified SNe that pass cosmology quality cuts. This sample spans the largest redshift (z) range ever covered by a single SN survey (0.1 < z < 1.13) and is the largest single sample from a single instrument of SNe ever used for cosmological constraints. We describe in detail the improvements made to obtain the final DES-SN photometry and provide a comparison to what was used in the 3 yr DES-SN spectroscopically confirmed Type Ia SN sample. We also include a comparative analysis of the performance of the SMP photometry with respect to the real-time DiffImg forced photometry and find that SMP photometry is more precise, more accurate, and less sensitive to the host-galaxy surface brightness anomaly. The public release of the light curves and ancillary data can be found at github.com/des-science/DES-SN5YR and doi:10.5281/zenodo.12720777.

79 ASTRONOMY AND ASTROPHYSICS↗

A cumulus cloud field observed by Landsat Thematic Mapper

The development of a spatial coherence scene model which is to be utilized to determine cloud properties from TM data is described. The observed radiances are modeled in terms of ocean and cloud spectra, the cloud fraction, and a geometric factor. Consideration is given to saturation effects and the need to constrain the model. The conjugate gradient algorithm is utilized to fit the model. A one-dimensional simulation was performed in order to evaluate the model; it is observed that the model provides good fit.

Hoffman, Ross N.↗