A primary objective of the GOES-16 post-launch airborne science field campaign was to provide an independent validation of the SI traceability of the Advanced Baseline Imager (ABI) spectral radiance observations for all detectors post-launch. The GOES-16 field campaign conducted sixteen validation missions (March to May 2017), three of which served as the primary ABI validation missions and are the focus of this work. These validation missions were conducted over ideal Earth targets with an integrated set of well characterized hyperspectral reference sensors aboard a high-altitude NASA ER-2
aircraft. These missions required ABI special collections (to scan all detectors over the earth targets), unique aircraft maneuvers, coordinated ground validation teams, and a diplomatic flight clearance with the Mexican Government. This effort presents a detector-level deep-dive analysis of data from the targeted sites using novel geospatial database and image abstraction techniques to select and process matching pixels between ABI and reference instruments. The ABI reflective solar band performance (ABI bands 1-3 & 5-6) was found to have biases within 5 % radiance for all bands, except band 2; and the ABI thermal emissive band performance was found to have biases within 1 K for all bands. Additional inter-comparison results using targeted ABI special collections with the Low Earth Orbit reference sensor S-NPP/VIIRS will also be discussed. The reference data collected from the campaign has demonstrated that the ABI SI traceability has been
validated post-launch and established a new performance benchmark for NOAA’s next generation geostationary Earth observing instrument products.
The GOES-16 Advanced Baseline Imager (ABI) is the first of four of NOAA's new generation of Earth imagers. The ABI uses large focal plane arrays (100s to 1000s of detectors per channel), which is a significant increase in the number of detectors per channel compared to the heritage GOES O-P imagers (2 to 8 detectors per channel). Due to the increase in number of detectors there is an increased risk of imaging striping in the L1b & L2+ products. To support post-launch striping risk mitigation strategies, customized ABI special scans (ABI North South Scans - NSS) were developed and implemented in the post-launch checkout validation plan. ABI NSS collections navigate each detector of a given channel over the same Earth target enabling the characterization of detector-level performance evaluation. These scans were used to collect data over several Earth targets to understand detector-to-detector uniformity as function of a broad set of targets. This effort will focus on the data analysis, from a limited set of NSS data (ABI Ch. 1), to demonstrate the fundamental methodology and ability to conduct post-launch detector-level performance characterization and advanced relative calibrations using such data. These collections and results provide critical insight in the development of striping risk mitigation strategies needed in the GOES-R era to ensure L1b data quality to the GOES user community.
The Advanced Baseline Imager (ABI) is a critical instrument onboard GOES-16 which provides high quality Reflective Solar Bands (RSB) data though radiometric calibration using onboard solar diffuser. Intensive field campaign for post-launch validation of the ABI L1B spectral radiance observations was carried out during March-May, 2017 to ensure the SI traceability of ABI. In this paper, radiometric calibrations of the five RSBs of ABI are evaluated with the measurements by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) onboard the high-altitude aircraft ER2. The ABI MESO data processed by the vendor with ray-matching to AVIRIS-NG during the field campaign was compared with the AVIRIS-NG measurements for radiometric bias evaluation. Furthermore, there were several implementations and updates in the solar calibration of ABI RSBs which resulted in different versions of detector gains and nonlinear calibration factors. These calibrations included the calibration by the operational ground processing system, by vendor and the calibration with updated nonlinear calibration factor table for striping mitigation and accounting for the integration time difference between solar calibration and Earth view. The North-South Scan (NSS) field campaign data of ABI were re-processed with these calibration coefficients to quantitatively evaluate the detector uniformity change. The detector uniformity difference are traced back to the difference in the implementation of the solar calibration.
Georeferenced data of various modalities are increasingly available for intelligence and commercial use, however effectively exploiting these sources demands a unified data space capable of capturing the unique contribution of each input. This work presents a suite of software tools for representing geospatial vector data and overhead imagery in a shared high-dimension vector or embedding" space that supports fused learning and similarity search across dissimilar modalities. While the approach is suitable for fusing arbitrary input types, including free text, the present work exploits the obvious but computationally difficult relationship between GIS and overhead imagery. GIS is comprised of temporally-smoothed but information-limited content of a GIS, while overhead imagery provides an information-rich but temporally-limited perspective. This processing framework includes some important extensions of concepts in literature but, more critically, presents a means to accomplish them as a unified framework at scale on commodity cloud architectures.
Hyperspectral imaging sensors commonly employ multiple apertures or focal planes for broad spectral coverage. These designs often result in spatial misregistration artifacts between the spectral regions. Unknown misregistration errors of fractions of a pixel cannot be corrected and can have a negative impact on target detection performance, especially for targets that are subpixel. The work here analyzes the impact of band-to-band misregistration on hyperspectral target detection performance. Synthetic imagery was used to simulate various amounts of sub-pixel misregistration between the visible (VIS) and near infrared (NIR) regions of the optical spectrum. Scenes were created with vehicles placed as targets. Target detection algorithms were applied using both the full spectrum misregistered imagery, and the VIS and NIR bands separately. Receiver operating characteristic curves were used to assess the performance of each algorithm for each target. Results indicate that statistical target detection algorithms are less sensitive to band-to-band misregistration than geometric algorithms. Results also indicate that in some cases it is more beneficial to use full spectrum misregistered imagery rather than applying target detection algorithms to the VIS and NIR bands separately, even for large amounts of sub-pixel misregistration.
Many hyperspectral sensors collect data using multiple spectrometers to span a broad spectral range. These
spectrometers can be fed by optical fibers on the same or separate focal planes. The Modular Imaging Spectrometer
Instrument (MISI), a 70 band line scanner built by the Rochester Institute of Technology, is configured
in this manner. Visible and near infrared spectrometers at the primary focal plane are each fed by their own
optical fiber. The spatial offset between the two fibers on the focal plane causes an inherent misregistration
between the two sets of spectral bands. This configuration causes a relatively complicated misregistration which
cannot be corrected with a simple shift or rotation of the data. This mismatch between spectral channels has
detrimental effects on the spectral purity of each pixel, especially when dealing with the application of sub-pixel
target detection. A geometric model of the sensor has been developed to solve for the misregistration and achieve
image rectification. This paper addresses the issues in dealing with the misregistration and techniques used to
improve spectral purity on a per pixel basis.
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