Assessing Lava Flow Subpixel Surface Roughness and Particle Size Distribution for Improved Thermal Inertia Interpretations
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Christine Simurda↗
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Quantifying the Impacts of Subpixel Reflectance Variability on Cloud Optical Thickness and Effective Radius Retrievals Based On High‐Resolution ASTER Observations
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F. Werner↗
Subpixel translation-registration of random fields
The author examines both theoretically and through a simulation study the feasibility of identifying the location within a large reference gray-level array of a smaller sensed array to an accuracy finer than one pixel. It is assumed that the sensed image before discretization into pixels consists of a translated, but not rotated, section of the reference image with some superposed noise. The theoretical and empirical results show that when the noise has standard deviation no larger than that of a realistic reference field, the upper quartile of the registration error is on the order of 0.25-0.5 pixels.
Slud, Eric V.↗
Effects of subpixel image restoration on digital correlation error estimates
The results of initial studies to determine the key parameters influencing the performance of a computer-based measurement system are presented. The system components were modeled, and a representative intensity pattern was chosen and deformed by known amounts. The effect of varying different model parameters in the model was analyzed numerically. The most significant parameters were found to be: (1) the number of quantization levels in the digitization process, (2) the ratio of the frequency of the signal to the frequency of the sampling, and (3) the form of the intensity interpolation function.
Sutton, Michael A.↗
Centroid position measurements and subpixel sensitivity variations with the MAMA detector
Initial measurements of the position sensitivity of a visible-light multianode microchannel array detector show that centroid calculations for image spots are accurate to better than 0.04 pixels even with sources that are essentially delta functions at the photocathode.Subpixel sensitivity variations of 10-15 percent are typically found for pixels in the array. Variations as large as 30 percent are possible in the worst conditions. These variations limit the photometric accuracy of the detector when very small scale features are observed. The photometric accuracy and the position sensitivity of the detector appear to be limited by cross-coupling effects within the anode array. Initial measurements with more recent designs of the detector show that most or all of this cross-coupling has been eliminated.
Morgan, Jeffrey S.↗
Subpixel resolution from multiple images
Multiple images taken from similar locations and under similar lighting conditions contain similar, but not identical, information. Slight differences in instrument orientation and position produces mismatches between the projected pixel grids. These mismatches ensure that any point on the ground is sampled differently in each image. If all the images can be registered with respect to each other to a small fraction of a pixel accuracy, then the information from the multiple images can be combined to increase linear resolution by roughly the square root of the number of images. In addition, the gray-scale resolution of the composite image is also improved. We describe methods for multiple image registration and combination, and discuss some of the problems encountered in developing and extending them. We display test results with 8:1 resolution enhancement, and Viking Orbiter imagery with 2:1 and 4:1 enhancements.
Cheeseman, Peter↗
Subpixel Snow-Covered-Area and Snow Grain Size From Mixture Analysis with AVIRIS Data
Snow-covered-area (SCA) and snow grain size are crucial inputs to hydrologic and climatologic modeling of alpine and other seasonally snow-covered regions. SCA is necessary to parameterize energy budget calculations in climate models, to determine in which regions point snowmelt models are to be run for distributed snowmelt modeling efforts and to provide a basis from which estimates of snow water equivalent (SWE) may be made. Snow grain size, SWE and snow impurities determine the spectral albedo of snow, which controls the net solar flux at the snowpack surface. Snow albedo is of the utmost importance in snowmelt modeling, yet the difficulty with which grain size, SWE, and impurities are mapped has left the spatial distribution of snow albedo in alpine catchments poorly understood. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has been used to estimate sub-pixel snow-covered-area and snow grain size independently. In this paper we present a technique which improves estimates of both snow parameters by treating their mapping simultaneously.
Green Robert O.↗
Measurement of Dynamic Geologic Processes at Subpixel Scales
Methods have recently been developed for the utilization of remotely sensed image data in the measurement of terrain displacements resulting from geologic processes. In optical imagery, measurements precise to a fraction of a pixel are achieved by statistical image matching.
optical imagery interferometry terrain displacemen↗
Imageodesy: A Tool for Mapping Subpixel Terrain Displacements in Satellite Imagery
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Mapping↗
Development of Multi-Sensor Global Cloud and Radiance Composites for DSCOVR EPIC Imager with Subpixel Definition
The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). EPIC delivers adequate spatial resolution imagery but only in shortwave bands (317-780 nm), while NISTAR measures the top-of-atmosphere (TOA) whole-disk radiance in shortwave and longwave broadband windows. Accurate calculation of albedo and outgoing longwave flux requires a high-resolution scene identification such as the radiance observations and cloud properties retrievals from low earth orbit (LEO, including NASA Terra and Aqua MODIS, Suomi-NPP VIIRS, and NOAA AVHRR) and geosynchronous (GEO, including GOES east and west, METEOSAT, INSAT-3D, MTSAT-2, and Himawari-8) satellite imagers. The cloud properties are derived using the Clouds and the Earth's Radiant Energy System (CERES) mission Cloud Subsystem group algorithms. These properties have to be co-located with EPIC pixels to provide the scene identification and to select anisotropic directional models (ADMs), which are then used to adjust the NISTAR-measured radiance and subsequently obtain the global daytime shortwave and longwave fluxes. This work presents an algorithm for optimal merging of selected radiance and cloud property parameters derived from multiple satellite imagers to obtain seamless global hourly composites at 5-km resolution. Selection of satellite data for each 5-km pixel is based on an aggregated rating that incorporates five parameters: nominal satellite resolution, pixel time relative to the EPIC time, viewing zenith angle, distance from day/night terminator, and probability of sun glint. To provide a smoother transition in the merged output, in regions where candidate pixel data from two satellite sources have comparable aggregated rating, the selection decision is defined by the cumulative function of the normal distribution so that abrupt changes in the visual appearance of the composite data are avoided. Higher spatial accuracy in the composite product is achieved by using the inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling.
Khlopenkov, Konstantin V.↗
Subpixel Snow-Covered-Area and Snow Grain Size from Mixture Analysis with Aviris Data
Snow-covered-area (SCA) and snow grain size are crucial inputs to hydrologic and climatologic modeling of alpine and other seasonally snow-covered regions. SCA is necessary to parameterize energy budget calculations in climate models, to determine in which regions point snowmelt models are to be run for distributed snowmelt modeling efforts and to provide a basis from which estimates of snow water equivalent (SWE) may be made.
hydrologic↗
MODELING TRI-DIRECTIONAL REFLECTANCE DISTRIBUTION FUNTIONS (TRDF) WITH APPLICATION TO SUBPIXEL TARGET DETECTION.
Abstract not provided.
Zollweg, Joshua David↗
Improving Cloud Optical Property Retrievals for Partly Cloudy Pixels Using Coincident Higher-Resolution Single Band Measurements: A Feasibility Study Using ASTER Observations
Clear-sky contamination is a challenging and long-lasting problem for cloud optical thickness (τ) and effective droplet radius (r eff ) retrievals using passive satellite sensors. This study explores the feasibility of improving both τ and r eff retrievals for partly cloudy (PCL) pixels by using available subpixel samples in a visible to near-infrared band, which many satellite sensors offer. Data are provided by high-resolution reflectance (R ) observations and cloud property retrievals by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) at horizontal resolutions between 30-960m. For partly cloudy 960-m observations, the clear-sky component of the pixels induces significant underestimations of up to 58% for τ, while overestimations in r eff can exceed 41%. This yields underestimations in the derived liquid water path and cloud droplet number concentration of up to 68% and 72%, respectively. By means of three different assumptions it is shown that subpixel R observations in the visible to near-infrared band can be used to estimate higher-resolution R for the second band in the retrieval scheme, as well as the subpixel cloud cover. Here, the estimated values compare well to actually observed ASTER results and are used to retrieve cloud properties, which are unbiased by the clear-sky component of PCL pixels. While the presented retrieval approach is only evaluated for marine boundary layer clouds, it is computationally efficient and can be easily applied to observations from different imagers. As an example, the PCL retrieval scheme is applied to data by the Moderate Resolution Imaging Spectroradiometer, where similar biases for PCL pixels are observed.
54 ENVIRONMENTAL SCIENCES↗
A method for evaluating image quality of monochrome and color displays based on luminance by use of a commercially available color digital camera
Purpose: The aim of this study is to propose a method for the quantitative evaluation of image quality of both monochrome and color liquid-crystal displays (LCDs) using a commercially available color digital camera. Methods: The intensities of the unprocessed red (R), green (G), and blue (B) signals of a camera vary depending on the spectral sensitivity of the image sensor used in the camera. For consistent evaluation of image quality for both monochrome and color LCDs, the unprocessed RGB signals of the camera were converted into gray scale signals that corresponded to the luminance of the LCD. Gray scale signals for the monochrome LCD were evaluated by using only the green channel signals of the camera. For the color LCD, the RGB signals of the camera were converted into gray scale signals by employing weighting factors (WFs) for each RGB channel. A line image displayed on the color LCD was simulated on the monochrome LCD by using a software application for subpixel driving in order to verify the WF-based conversion method. Furthermore, the results obtained by different types of commercially available color cameras and a photometric camera were compared to examine the consistency of the authors’ method. Finally, image quality for both the monochrome and color LCDs was assessed by measuring modulation transfer functions (MTFs) and Wiener spectra (WS). Results: The authors’ results demonstrated that the proposed method for calibrating the spectral sensitivity of the camera resulted in a consistent and reliable evaluation of the luminance of monochrome and color LCDs. The MTFs and WS showed different characteristics for the two LCD types owing to difference in the subpixel structure. The MTF in the vertical direction of the color LCD was superior to that of the monochrome LCD, although the WS in the vertical direction of the color LCD was inferior to that of the monochrome LCD as a result of luminance fluctuations in RGB subpixels. Conclusions: The authors’ method based on the use of a commercially available color camera is useful to evaluate and understand the display performances of both monochrome and color LCDs in radiology departments.
60 APPLIED LIFE SCIENCES↗
Spatial Scaling of Snow Observations and Microwave Emission Modeling During CLPX and Appropriate Satellite Sensor Resolution
Accurate estimates of snow water equivalent and other properties play an important role in weather, natural hazard, and hydrological forecasting and climate modeling over a range of scales in space and time. Remote sensing-derived estimates have traditionally been of the "snapshot" type, but techniques involving models with assimilation are also being explored. In both cases, forward emission models are useful to understand the observed passive microwave signatures and developing retrieval algorithms. However, mismatches between passive microwave sensor resolutions and the scales of processes controlling subpixel heterogeneity can affect the accuracy of the estimates. Improving the spatial resolution of new passive microwave satellite sensors is a major desire in order to (literally) resolve such subpixel heterogeneity, but limited spacecraft and mission resources impose severe constraints and tradeoffs. In order to maximize science return while mitigating risk for a satellite concept, it is essential to understand the scaling behavior of snow in terms of what the sensor sees (brightness temperature) as well as in terms of the actual variability of snow. NASA's Cold Land Processes Experiment-1 (CLPX-1: Colorado, 2002 and 2003) was designed to provide data to measure these scaling behaviors for varying snow conditions in areas with forested, alpine, and meadow/pasture land cover. We will use observations from CLPX-1 ground, airborne, and satellite passive microwave sensors to examine and evaluate the scaling behavior of observed and modeled brightness temperatures and observed and retrieved snow parameters across scales from meters to 10's of kilometers. The conclusions will provide direct examples of the appropriate spatial sampling scales of new sensors for snow remote sensing. The analyses will also illustrate the effects and spatial scales of the underlying phenomena (e.g., land cover) that control subpixel heterogeneity.
Kim, Edward J.↗
Photodetector Arrays for Multicolor Visible/Infrared Imaging
Monolithic focal-plane arrays of photodetectors capable of imaging the same scenes simultaneously in multiple wavelength bands in the visible and infrared spectral regions have been proposed. In prior visible/infrared imaging systems, it has been standard practice to use separate optical trains to form images in visible and infrared wavelength bands on separate visibleand infrared-photodetector arrays. Because the proposal would enable the detection of images in multiple wavelength bands on the same focal plane, the proposal would make it unnecessary to use multiple optical trains. Hence, multispectral imaging systems could be made more compact and the difficulties of aligning multiple optical trains would be eliminated. Each pixel in an array according to the proposal would contain stacks of several photodetectors. The proposal is a logical extension of prior concepts of arrays of stacked photodetectors for imaging in two or three wavelength bands. For example, such an array was described in Three-Color Focal-Plane Array of Infrared QWIPs (NPO-20683), NASA Tech Briefs, Vol. 24, No. 5 (May 2000), page 26a. In one proposed design, (see figure), each pixel would be divided into four subpixels, one being dedicated to a visible- and-near-infrared (V) band, one to a combination of the V band and a verylong- wavelength infrared (VLWIR) band, one to a combination of the V band and a long-wavelength infrared (LWIR) band, and one to a combination of the V band and a medium-wavelength infrared (MWIR) band. For this purpose, each subpixel would include a GaAs-based positive/intrinsic/negative (PIN) photodiode for detection in the V band stacked with three quantum-well infrared photodetectors (QWIPs), each optimized for one of the aforementioned infrared bands. The stacks of photodetectors in all the subpixels would be identical except for the electrical connections, which would be configured to activate the various wavelengthband combinations.
Gunapala, Sarath↗
A physical method for downscaling land surface temperatures using surface energy balance theory
Fine-resolution land surface temperature (LST) derived from thermal infrared remote sensing images is a good indicator of surface water status and plays an essential role in the exchange of energy and water between land and atmosphere. A physical surface energy balance (SEB)-based LST downscaling method (DTsEB) is developed to downscale coarse remotely sensed thermal infrared LST products with fine-resolution visible and near-infrared data. Here, the DTsEB method is advantageous for its ability to mechanically interrelate surface variables contributing to the spatial variation of LST, to quantitatively weigh the contributions of each related variable within a physical framework, and to efficaciously avoid the subjective selection of scaling factors and the establishment of statistical regression relationships. The applicability of the DTsEB method was tested by downscaling 12 scenes of 990 m Moderate Resolution Imaging Spectroradiometer (MODIS) and aggregated Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST products to 90 m resolution at six overpass times between 2005 and 2015 over three 9.9 km by 9.9 km cropland (mixed by grass, tree, and built-up land) study areas. Three typical LST downscaling methods, namely the widely applied TsHARP, the later developed least median square regression downscaling (LMS) and the geographically weighted regression (GWR), were introduced for intercomparison. The results showed that the DTsEB method could more effectively reconstruct the subpixel spatial variations in LST within the coarse-resolution pixels and achieve a better downscaling accuracy than the TsHARP, LMS and GWR methods. The DTsEB method yielded, on average, root mean square errors (RMSEs) of 2.01 K and 1.42 K when applied to the MODIS datasets and aggregated ASTER datasets, respectively, which were lower than those obtained with the TsHARP method, with average RMSEs of 2.41 K and 1.71 K, the LMS method, with average RMSEs of 2.35 K and 1.63 K, and the GWR method, with average RMSEs of 2.38 K and 1.64 K, respectively. The contributions of the related surface variables to the subpixel spatial variation in the LST varied both spatially and temporally and were different from each other. In summary, the DTsEB method was demonstrated to outperform the TsHARP, LMS, and GWR methods and could be used as a good alternative for downscaling LST products from coarse to fine resolution with high robustness and accuracy.
54 ENVIRONMENTAL SCIENCES↗
Processing techniques development, volume 3
The author has identified the following significant results. Analysis of the geometric characteristics of the aircraft synthetic aperture radar (SAR) relative to LANDSAT indicated that relatively low order polynominals would model the distortions to subpixel accuracy to bring SAR into registration for good quality imagery. Also the area analyzed was small, about 10 miles square, so this is an additional constraint. For the Air Force/ERIM data, none of the tested methods could achieve subpixel accuracy. Reasons for this is unknown; however, the noisy (high scintillation) nature of the data and attendent unrecognizability of features contribute to this error. It is concluded that the quadratic model would adequately provide distortion modeling for small areas, i.e., 10 to 20 miles square.
Landgrebe, D. A.↗