Abstract
Abstract. The National Geospatial-Intelligence Agency (NGA) designed the Generic Linear Array Scanner (GLAS) model for geopositioning images from both airborne and spaceborne linear array scanning systems, including pushbroom, whiskbroom, and panoramic sensors. Providers of hyperspectral imagery (HSI) historically have not populated products with high fidelity metadata to support downstream photogrammetric processing. To demonstrate recommended metadata population and exploitation using the GLAS model, NGA has generated example HSI products using data collected by NASA’s EO-1 Hyperion sensor and provided courtesy of the U.S. Geological Survey. This paper provides novel techniques for: 1) generating reasonably accurate initial approximations for GLAS metadata as a function of per-image metadata consisting of only timing information and the latitude and longitude values of the four corners of the image; and 2) identifying a vector of adjustable parameters and reasonable values for its a priori error covariance matrix that enable corrections to the metadata during a bundle adjustment. The paper describes applying these techniques to fourteen overlapping Hyperion images of the Alps, running a bundle adjustment as a function of tie points and optional ground control points, and demonstrating superior results to the previous polynomial based approach as quantified by the 3D errors at several ground check points.
Highlights
1.1 Problem StatementThe use of a geometric sensor model and photogrammetric processing enables co-registration of multiple overlapping images, 3D extraction of the terrain surface, and alignment of new images that become available such that change detection can be applied
A team of scientists and software engineers built a library of sensor models, including the Generic Linear Array Scanner (GLAS) model, that has been incorporated into popular Electronic Light Tables (ELTs)
The CSEXRB Tagged Record Extension (TRE) contains the date of image acquisition, time tags associated with exposure of a specific line of an image or frame, number of lines and samples in the collected image, and Universally Unique Identifiers (UUIDs) to associate image segments containing GLAS/Generic Framesequence Model (GFM) TREs with GLAS/GFM Data Extension Segment (DES) in the same National Imagery Transmission Format (NITF) file
Summary
The use of a geometric sensor model and photogrammetric processing enables co-registration of multiple overlapping images, 3D extraction of the terrain surface, and alignment of new images that become available such that change detection can be applied. Providers of imagery to the photogrammetry and computer vision communities are known for providing high fidelity geopositioning metadata that can be recognized and applied by mapping software or research software either as a frame sensor model, potentially including lens distortions, or as rational polynomial coefficients (RPCs), which is a common replacement sensor model for linear array scanning imagery. Providers of hyperspectral imagery (HSI) historically have not supplied high fidelity metadata to support downstream photogrammetric processing. To support low-cost integration of high-quality multi- (MSI) and hyperspectral imagery, the NGA has ratified the Spectral National Imagery Transmission Format (NITF) Implementation Profile (SNIP) for spectral NITF datasets, which recommends the GLAS model as the preferred geopositioning metadata for spectral linear array scanners. To demonstrate conformance with the SNIP, NGA has generated example HSI products using data collected by NASA’s EO-1 Hyperion sensor and provided courtesy of the U.S Geological Survey
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