Processing Techniques for Topographic Mapping Using Interferometric SAR
On-going processor research relating to interferometry.
determination map resampling↗
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An overview of the thematic mapper geometric correction system
Geometric accuracy specifications for LANDSAT 4 are reviewed and the processing concepts which form the basis of NASA's thematic mapper geometric correction system are summarized for both the flight and ground segments. The flight segment includes the thematic mapper instrument, attitude measurement devices, attitude control, and ephemeris processing. For geometric correction the ground segment uses mirror scan correction data, payload correction data, and control point information to determine where TM detector samples fall on output map projection systems. Then the raw imagery is reformatted and resampled to produce image samples on a selected output projection grid system.
Beyer, E. P.↗
A post-processing system for automated rectification and registration of spaceborne SAR imagery
An automated post-processing system has been developed that interfaces with the raw image output of the operational digital SAR correlator. This system is designed for optimal efficiency by using advanced signal processing hardware and an algorithm that requires no operator interaction, such as the determination of ground control points. The standard output is a geocoded image product (i.e. resampled to a specified map projection). The system is capable of producing multiframe mosaics for large-scale mapping by combining images in both the along-track direction and adjacent cross-track swaths from ascending and descending passes over the same target area. The output products have absolute location uncertainty of less than 50 m and relative distortion (scale factor and skew) of less than 0.1 per cent relative to local variations from the assumed geoid.
Curlander, John C.↗
Bi-resampled data study
The results of an experimental study conducted to determine the geometric and radiometric effects of double resampling (bi-resampling) performed on image data in the process of performing map projection transformations are reported.
Benner, R.↗
Monitoring biological impacts of space shuttle launches from Vandenberg Air Force Base: Establishment of baseline conditions
Space shuttle launches produce environmental impacts resulting from the formation of an exhaust cloud containing hydrogen chloride aerosols and aluminum oxide particulates. Studies have shown that most impacts occur near-field (within 1.5 km) of the launch site while deposition from launches occurs far-field (as distant as 22 km). In order to establish baseline conditions of vegetation and soils in the areas likely to be impacted by shuttle launches from Vandenberg Air Force Base (VAFB), vegetation and soils in the vicinity of Space Launch Complex-6 (SLC-6) were sampled and a vegetation map prepared. The areas likely to be impacted by launches were determined considering the structure of the launch complex, the prevailing winds, the terrain, and predictions of the Rocket Exhaust Effluent Diffusion Model (REEDM). Fifty vegetation transects were established and sampled in March 1986 and resampled in September 1986. A vegetation map was prepared for six Master Planning maps surrounding SLC-6 using LANDSAT Thematic Mapper imagery as well as color and color infrared aerial photography. Soil samples were collected form the 0 to 7.5 cm layer at all transects in the wet season and at a subsample of the transects in the dry season and analyzed for pH, organic matter, conductivity, cation exchange capacity, exchangeable Ca, Mg, Na, K, and Al, available NH3-N, PO4-P, Cu, Fe, Mn, Zn, and TKN.
Schmaizer, Paul A.↗
Evaluation of the radiometric quality of the TM data using clustering and multispectral distance measures
Radiometrically and geometrically corrected TM data from three different geographic locations were examined. Histograms were inspected for each band to determine the dynamic range of the data, the shape of the distributions, and to verify whether empty bins were introduced by the radiometric correction process. The effect of geometric correction on the radiometry of the resampled pixels was determined. The information content between TM and MSS data sets were compared and the TM data were used to map the thermal effluent discharge into a river ecosystem from a nuclear thermal power plant, and application only possible previously only possible through the acquisition of thermal infrared scanner data from aircraft altitudes.
Bartolucci, L. A.↗
A Landsat Digital Image Rectification System
DIRS is a Digital Image Rectification System for the geometric correction of Landsat Multispectral Scanner digital image data. DIRS removes spatial distortions from the data and brings it into conformance with the Universal Transverse Mercator (UTM) map projection. Scene data in the form of landmarks or Ground Control Points (GCPs) are used to drive the geometric correction algorithms. The system offers extensive capabilities for 'shade printing' to aid in the determination of GCPs. Affine, two dimensional least squares polynominal and spacecraft attitude modeling techniques for geometric mapping are provided. Entire scenes or selected quadralaterals may be rectified. Resampling through nearest neighbor or cubic convolution at user designated intervals is available. The output products are in the form of digital tape in band interleaved, single band or CCT format in a rotated UTM projection. The system was designed and implemented on large scale IBM 360 computers with at least 300-500K bytes of memory for user application programs and five nine track tapes plus direct access storage.
Van Wie, P.↗
Correction and geological analysis of lunar 3.8 CM radar data
Several 3.8 cm radar frames were calibrated empirically by histogram fitting, because no instrument background data is available. Then data were corrected for geometric distortion by: (1) redetermining position of individual frames using most accurate recent lunar ephemerides; (2) reprojecting frames into simple cylindrical map projection; (3) using most recent catalog of lunar craters to determine the exact positions of features identifiable on radar frames; and (4) correcting for apparent distortion (misplacement of features in frames) by resampling using a different bilinear interpolation derived for each of the parallelopideds of the set defined for each frame. A hardcopy set of corrected frames was produced. Attempts to produce a mosaic of such corrected frames continue. The resulting mosaic can be used to show the systematic relationship between photographic thermal IR and radar data at different wavelengths in a region dominated by both mare and highland terrain.
Clark, P. E.↗
Geometric correction process for the Landsat-4 Thematic Mapper
The processing approaches which form the basis of the Landsat-4 Thematic Mapper (TM) geometric correction system are summarized. The correction is accomplished in two phases. In the first phase, correction data are generated. The spacecraft position, TM frame attitude, TM scanning mirror position, and detector sampling are found as a function of time through a combination of flight segment measurements, ground segment modeling, and control point information. The data, along with the earth position and geoid model, are used to determine the earth location for each TM image sample. Then, using map projections, correction data can be generated which define the location of each TM image sample on the output coordinate system. In the second phase, the correction data are used to resample TM detector samples onto the output coordinate system.
Beyer, E. P.↗
AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]
Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.
Smith, Kandler [National Renewable Energy Lab. (NR↗