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.
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 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.
As the number of pixels continue to grow in consumer and scientific imaging devices, it has become feasible to collect the
incident light field. In this paper, an imaging device developed around light field imaging is used to collect multispectral
and polarimetric imagery in a snapshot fashion. The sensor is described and a video data set is shown highlighting the
advantage of snapshot spectral imaging. Several novel computer vision approaches are applied to the video cubes to
perform scene characterization and target identification. It is shown how the addition of spectral and polarimetric data to
the video stream allows for multi-target identification and tracking not possible with traditional RGB video collection.
A multi-modal (hyperspectral, multispectral, and LIDAR) imaging data collection campaign was conducted just south of Rochester New York in Avon, NY on September 20, 2012 by the Rochester Institute of Technology (RIT) in conjunction with SpecTIR, LLC, the Air Force Research Lab (AFRL), the Naval Research Lab (NRL), United Technologies Aerospace Systems (UTAS) and MITRE. The campaign was a follow on from the SpecTIR Hyperspectral Airborne Rochester Experiment (SHARE) from 2010. Data was collected in support of the eleven simultaneous experiments described here. The airborne imagery was collected over four different sites with hyperspectral, multispectral, and LIDAR sensors. The sites for data collection included Avon, NY, Conesus Lake, Hemlock Lake and forest, and a nearby quarry. Experiments included topics such as target unmixing, subpixel detection, material identification, impacts of illumination on materials, forest health, and in-water target detection. An extensive ground truthing effort was conducted in addition to collection of the airborne imagery. The ultimate goal of the data collection campaign is to provide the remote sensing community with a shareable resource to support future research. This paper details the experiments conducted and the data that was collected during this campaign.
Passive polarimetery has been used for many different remote sensing applications and can provide useful signatures for
certain types of phenomenology. While broadband polarimeters have many advantages, such as high signal-to-noise ratio
and small ground sample distance, there has been growing interest in the development of algorithms that take advantage
of both spectral and polarimetric signals. The Multispectral Aerial Passive Polarimeter System (MAPPS) aims to produce
multispectral polarimetric imagery of test scenes that can be used in algorithm development efforts and phenomenology
studies. Preliminary data is presented along with a calibration and processing workflow that produces registered spectral
Stokes imagery.
Liquid crystal tunable filters (LCTFs) are a technology that can act as both a spectral and linear polarization filter for an imaging device. Paired with the appropriate hardware, a LCTF can be configured to collect hyperspectral Stokes imagery which contains both spectral as well as polarimetric information on a per-pixel level basis. This data is used to investigate the utility of spectro-polarimetric data with standard spectral analysis algorithms, in this case anomaly detection. A method to simulate different ground sample distances (GSDs) is used to illustrate the effect on algorithm performance. In this paper, a spectro-polarimetric imager is presented that can collect spectro-polarimetric image cubes in units of calibrated sensor reaching radiance. The system is used to collect imagery of two scenes, each containing die-cast scale vehicles and different background types. An anomaly detector is applied to the intensity and polarized image cubes to find those pixels that are different from the background spectrally and/or polarimetrically. The effect of changing the apparent GSD on the anomaly detection performance is explored. This shows that applying anomaly detection to spectro-polarimetric data can improve the false alarm rate over standard spectral data for finding certain types of man-made objects in complex backgrounds.
In the task of automated anomaly detection, it is desirable to find regions within imagery that contain man-made structures
or objects. The task of separating these signatures from the scene background and other naturally occurring anomalies
can be challenging. This task is even more difficult when the spectral signatures of the man-made objects are designed to
closely match the surrounding background. As new sensors emerge that can image both spectrally and polarimetrically, it
is possible to utilize the polarimetric signature to discriminate between many types of man-made and natural anomalies.
One type of passive imaging system that allows for spetro-polarimetric data to be collected is the pairing of a liquid crystal
tunable filter (LCTF) with a CCD camera thus creating a spectro-polarimetic imager (SPI). In this paper, an anomaly
detection scheme is implemented which makes use of the spectral Stokes imagery collected by this sensing system. The
ability for the anomaly detector to find man-made objects is assessed as a function of the number of spectral bands available
and it is shown that low false alarm rates can be achieved with relatively few spectral bands.
The small island nation of Haiti was devastated in early 2010 following a massive 7.0 earthquake that brought about
widespread destruction of infrastructure, many deaths and large-scale displacement of the population in the nation's
major cities. The World Bank and ImageCat, Inc tasked the Rochester Institute of Technology's (RIT) Wildfire Airborne
Sensor Platform (WASP) to gather a multi-spectral and multi-modal assessment of the disaster over a seven-day period
to be used for relief and reconstruction efforts.
Traditionally, private sector aerial remote sensing platforms work on processing and product delivery timelines
measured in days, a scenario that has the potential to reduce the value of the data in time-sensitive situations such as
those found in responding to a disaster. This paper will describe the methodologies and practices used by RIT to deliver
an open set of products typically within a twenty-four hour period from when they were initially collected.
Response to the Haiti disaster can be broken down into four major sections: 1) data collection and logistics, 2)
transmission of raw data from a remote location to a central processing and dissemination location, 3) rapid image
processing of a massive amount of raw data, and 4) dissemination of processed data to global organizations utilizing it to
provide the maximum benefit. Each section required it's own major effort to ensure the success of the overall mission. A
discussion of each section will be provided along with an analysis of methods that could be implemented in future
exercises to increase efficiency and effectiveness.
For sensing systems that characterize the spectro-polarimetric radiance reaching the camera, the origin of the sensed phenomenology is a complex mixture of sources. While some of these sources do not contribute to the polarimetric signature, many do such as the polarization state of the downwelled sky radiance, the target and background p-BRDF(polarimetric bidirectional reflectance distribution function), the polarization state of the upwelled path radiance, and the sensor Mueller matrix transfer function. In this paper we derive portions of the p-BRDF in terms of both the spectral diffuse and polarimetric specular components of the reflectance using an in-scene calibration technique. This process is applied to simulated data, laboratory data, and data from a field collection. Spectra of a car panel for clean and contaminated states derived using laboratory data are injected into a hyperspectral image cube. It is shown how this target can be identified using a target specific tracking vector derived from its polarimetric signature as it moves between spatial locations within a scene.
The characterization of material reflectance properties is important in the analysis of hyperspectral and polarization
imagery as well as accurate simulation of such images. This paper merges the results of empirical reflectance property
(spectral pBRDF) measurements with detailed model based simulations. The empirical data are collected with a
laboratory spectroradiometer as well as an RIT-developed spectro-polarimetric imaging goniometer. The modeling uses
an adaptation of RIT's Digital Imaging and Remote Sensing Image Generation (DIRSIG) model to capture the radiative
transfer in rough surfaces with micron-scale features. Measurements and model results for several man-made materials
under various conditions are presented.
The ALGE code is a hydrodynamic model developed by Savannah River National Laboratory (SRNL) to derive
the power output levels of an electric generation facility from observing the associated cooling pond with an aerial
imaging platform. Over the past two years work has been completed to extend the capabilities of the model to
incorporate snow and ice as possible phenomena in the modeled environment. In order to validate the extension
of the model, intensive ground truth data as well as high-resolution aerial infrared imagery were collected during
the winters of 2008-2009 and 2009-2010, for a combined eight months of data collection. Due to the harsh and
extreme environmental conditions automatic data collection instruments were designed and deployed. Based on
experience gained during the first collection season and equipment design failures, overhauls in the design and
operation of the automated data collection buoys were performed. In addition, a more thorough and robust twofold
calibration technique was implemented within the aerial imaging chain to assess the accuracy of the retrieved
surface temperatures. By design, the calibration method employed in this application uses ground collected, geolocated
water surface temperatures and in-flight blackbody imagery to produce accurate temperature maps of
the pond in interest. A sensitivity analysis was implemented within the data reduction technique to produce
accurate sensor reaching temperature values using designed equipment and methods for temperature retrieval at
the water's surface.
For sensing systems that characterize the spectro-polarimetric radiance reaching the camera, the origin of the
sensed phenomenology is a complex mixture of sources. While some of these sources do not contribute to
the polarimetric signature, many do such as the downwelled sky polarization, the target and background p-
BRDF(polarimetric bi-directional reflectance distribution function), the upwelled sky polarization, and the camera
Mueller matrix transfer function. In this paper we investigate candidate in-scene calibration materials
potentially allowing for portions of the p-BRDF to be derived for material surfaces throughout the scene. Extraction
of target p-BRDF from the sensed spectro-polarimetric energy may result in improved target detection
performance in the future. Results using both synthetic and real data are presented.
The effectiveness of a power generation site's cooling pond has a significant impact on the overall efficiency of a
power plant. The ability to monitor a cooling pond using thermal remote sensing, coupled with hydrodynamic
models, is a valuable tool for determining the driving characteristics of a cooling system. However, the thermodynamic
analysis of a cooling lake can become significantly more complex when a power generation site is located
in a northern climate. The heated effluent from a power plant entering a cooling lake is often not enough to keep
a lake from freezing during winter months. Once the lake is partially or fully frozen, the predictive capabilities
of the hydrodynamic model are weakened due to an insulating surface layer of ice and snow. Thermal imagery
of a cooling pond was collected over a period of approximately 16 weeks in tandem with high-density thermal
measurements both in open water and embedded in ice, meteorological data, and snow layer characterization
data. The proposed research presents a method to employ thermal imagery to improve the performance of a 3-D
hydrodynamic model of a power plant cooling pond in the presence of ice and snow.
Many algorithms exist to invert airborne imagery from units of either radiance or sensor specific digital counts to units of reflectance. These compensation algorithms remove unwanted atmospheric variability allowing objects on the ground to be analyzed. Low error levels in homogenous atmospheric conditions have been demonstrated. In many cases however, clouds are present in the atmosphere which introduce error into the inversion at unacceptable levels. For example, the relationship that is defined between sensor reaching radiance and ground reflectance in a cloud free scene will not be the same as in an adjacent region with clouds in the surround. A novel method has been developed which utilizes ground based measurements to modify the empirical line method (ELM) approach on a per-pixel basis. A physics based model of the atmosphere is used to generate a spatial correction for the ELM. Creation of this model is accomplished by analyzing whole-sky imagery to produce a cloud mask which drives input parameters to the radiative transfer (RT) code MODTRAN. The RT code is run for several different azimuth and zenith orientations to create a three-dimensional representation of the hemisphere. The model is then used to achieve a per-pixel correction by adjusting the ELM slope spatially. This method is applied to real data acquired over the atmospheric radiation measurement (ARM) site in Lamount, OK. Performance of the method is evaluated with the Hyperspectral Digital Imagery Collection Experiment (HYDICE) instrument. The sensitivity to spectral sampling is also assessed by down-sampling the HYDICE data to the spectral response of the multi-spectral system Wildfire Airborne Sensor Program LITE (WASP Lite). Finally a method to utilize this approach when additional sensors (like a sky camera) are not available is suggested.
Many algorithms exist to invert airborne imagery from units of either radiance or sensor specific digital counts to
units of reflectance. These compensation algorithms remove unwanted atmospheric variability allowing objects on
the ground to be analyzed. Low error levels in homogenous atmospheric conditions have been demonstrated. In
many cases however, clouds are present in the atmosphere which introduce error into the inversion at unacceptable
levels. For example, the relationship that is defined between sensor reaching radiance and ground reflectance in
a cloud free scene will not be the same as in a scene with clouds. A novel method has been developed which
utilizes ground based measurements to modify the empirical line method (ELM) approach on a per-pixel basis.
A physics based model of the atmosphere is used to generate a spatial correction for the ELM. Creation of this
model is accomplished by analyzing whole-sky imagery to produce a cloud mask which drives input parameters
to the radiative transfer (RT) code MODTRAN. The RT code is run for several different azimuth and zenith
orientations to create a three-dimensional representation of the hemisphere. The model is then used to achieve
a per-pixel correction by adjusting the ELM slope spatially. This method is applied to real data acquired over
the atmospheric radiation measurement (ARM) site in Lamount, OK. Performance of the method is evaluated
with the Hyperspectral Digital Imagery Collection Experiment (HYDICE) instrument as well as a simulated
multi-spectral system.
A data collection experiment was held on 10 May 2006 in which various types of ground truth were collected. During analysis of the data, it became apparent that a good deal of temporal and spatial variability existed both between the two collection sites and between each of the flight lines. The variability was primarily manifested in the total sky radiance spectra collected during each flight line. These effects were believed to be due to a rapidly changing sky environment populated by sparse clouds that grew thicker as the collection continued. After discussing the theoretical considerations of remotely sensing in the presence of clouds and other objects, the results of our analysis are presented. It was observed that temporal variability resulted in approximately 2-15% error in sky radiance across the visible wavelengths, and that spatial variability in sky radiance could be shown to be responsible for up to 5% error in retrieved reflectance. Additional theoretical work in modeling the expected radiance contributions of a cloudy sky and the impacts of cloud-induced variability is on-going and will be supported by these results.
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