Dr. John R. Schott Profile

Dr. John R. Schott

Research Professor at Rochester Institute of Technology

SPIE Involvement:

Fellow status | Conference Program Committee | Author

Area of Expertise:

Remote Sensing

Publications (108)

PROCEEDINGS ARTICLE | September 5, 2017

Landsat-8 TIRS thermal radiometric calibration status

Proc. SPIE. 10402, Earth Observing Systems XXII

KEYWORDS: Infrared sensors, Landsat, Imaging systems, Sensors, Calibration, Satellites, Radiometry, Algorithm development, Stray light, Earth observing sensors

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PROCEEDINGS ARTICLE | September 5, 2017

Landsat 8 TIRS calibration with external sensors

Proc. SPIE. 10402, Earth Observing Systems XXII

KEYWORDS: Thermography, MODIS, Landsat, Sensors, Calibration, Satellites, Stray light, Sensor calibration, Atmospheric modeling, Temperature metrology

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SPIE Journal Paper | January 2, 2017

Image quality modeling and characterization of Nyquist-sampled framing sensors with operational considerations for remote sensing

Garma, Rey Jan , Schott, John , Fiete, Robert , Qiao, Jie , McKeown, Donald

OE Vol. 56 Issue 01

KEYWORDS: Signal to noise ratio, Sensors, Sensor performance, Image quality, Modulation transfer functions, Quantum efficiency, Remote sensing, Optical engineering, Photography, Electro optical modeling

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PROCEEDINGS ARTICLE | December 14, 2016

Enhancing moderate-resolution ocean color products over coastal/inland waters (Conference Presentation)

Proc. SPIE. 9999, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2016

KEYWORDS: MODIS, Landsat, Imaging systems, Remote sensing, Reflectivity, Space operations, Earth observing sensors, Current controlled current source

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PROCEEDINGS ARTICLE | October 19, 2016

Simulating the directional, spectral and textural properties of a large-scale scene at high resolution using a MODIS BRDF product

Proc. SPIE. 10000, Sensors, Systems, and Next-Generation Satellites XX

KEYWORDS: MODIS, Data modeling, Sensors, Reflectivity, Computer simulations, Bidirectional reflectance transmission function, Volume rendering, High dynamic range imaging, Spatial resolution, Scene simulation

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Many remote sensing applications rely on simulated scenes to perform complex interaction and sensitivity studies that are not possible with real-world scenes. These applications include the development and validation of new and existing algorithms, understanding of the sensor's performance prior to launch, and trade studies to determine ideal sensor configurations. The accuracy of these applications is dependent on the realism of the modeled scenes and sensors. The Digital Image and Remote Sensing Image Generation (DIRSIG) tool has been used extensively to model the complex spectral and spatial texture variation expected in large city-scale scenes and natural biomes. In the past, material properties that were used to represent targets in the simulated scenes were often assumed to be Lambertian in the absence of hand-measured directional data. However, this assumption presents a limitation for new algorithms that need to recognize the anisotropic behavior of targets. We have developed a new method to model and simulate large-scale high-resolution terrestrial scenes by combining bi-directional reflectance distribution function (BRDF) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data, high spatial resolution data, and hyperspectral data. The high spatial resolution data is used to separate materials and add textural variations to the scene, and the directional hemispherical reflectance from the hyperspectral data is used to adjust the magnitude of the MODIS BRDF. In this method, the shape of the BRDF is preserved since it changes very slowly, but its magnitude is varied based on the high resolution texture and hyperspectral data. In addition to the MODIS derived BRDF, target/class specific BRDF values or functions can also be applied to features of specific interest. The purpose of this paper is to discuss the techniques and the methodology used to model a forest region at a high resolution. The simulated scenes using this method for varying view angles show the expected variations in the reflectance due to the BRDF effects of the Harvard forest. The effectiveness of this technique to simulate real sensor data is evaluated by comparing the simulated data with the Landsat 8 Operational Land Image (OLI) data over the Harvard forest. Regions of interest were selected from the simulated and the real data for different targets and their Top-of-Atmospheric (TOA) radiance were compared. After adjusting for scaling correction due to the difference in atmospheric conditions between the simulated and the real data, the TOA radiance is found to agree within 5 % in the NIR band and 10 % in the visible bands for forest targets under similar illumination conditions. The technique presented in this paper can be extended for other biomes (e.g. desert regions and agricultural regions) by using the appropriate geographic regions. Since the entire scene is constructed in a simulated environment, parameters such as BRDF or its effects can be analyzed for general or target specific algorithm improvements. Also, the modeling and simulation techniques can be used as a baseline for the development and comparison of new sensor designs and to investigate the operational and environmental factors that affects the sensor constellations such as Sentinel and Landsat missions.

PROCEEDINGS ARTICLE | September 19, 2016

Modeling forest defoliation using simulated BRDF and assessing its effect on reflectance and sensor reaching radiance

Proc. SPIE. 9975, Remote Sensing and Modeling of Ecosystems for Sustainability XIII

KEYWORDS: Near infrared, Sun, Data modeling, Sensors, Remote sensing, Photons, Bidirectional reflectance transmission function, Atmospheric modeling, Process modeling, Visibility

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PROCEEDINGS ARTICLE | September 19, 2016

Landsat-7 ETM+ radiometric calibration status

Proc. SPIE. 9972, Earth Observing Systems XXI

KEYWORDS: MODIS, Landsat, Sensors, Calibration, Databases, Satellites, Lamps, Reflectivity, Photonic integrated circuits, Earth observing sensors

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PROCEEDINGS ARTICLE | May 17, 2016

Developing a confidence metric for the Landsat land surface temperature product

Proc. SPIE. 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII

KEYWORDS: Landsat, Sensors, Satellites, Error analysis, Clouds, Satellite imaging, Atmospheric monitoring, Earth observing sensors, Knowledge management, Temperature metrology

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PROCEEDINGS ARTICLE | May 17, 2016

Modeling of forest canopy BRDF using DIRSIG

Proc. SPIE. 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII

KEYWORDS: Sun, Data modeling, Sensors, Remote sensing, Reflectivity, Computer simulations, 3D modeling, Bidirectional reflectance transmission function, Digital imaging, Transmittance, Modeling and simulation, Virtual reality, Natural surfaces, Electroluminescent displays, Spectral models

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PROCEEDINGS ARTICLE | September 8, 2015

Requirement sensitivity studies for a future Landsat sensor

Proc. SPIE. 9607, Earth Observing Systems XX

KEYWORDS: Point spread functions, Landsat, Imaging systems, Sensors, Satellites, Remote sensing, Manufacturing, Modulation transfer functions, Tolerancing, Earth observing sensors

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PROCEEDINGS ARTICLE | September 8, 2015

Atmospheric correction for Landsat 8 over case 2 waters

Proc. SPIE. 9607, Earth Observing Systems XX

KEYWORDS: Signal to noise ratio, Near infrared, Short wave infrared radiation, Landsat, Water, Remote sensing, Calcium, Atmospheric corrections, Algorithm development, Earth observing sensors

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PROCEEDINGS ARTICLE | September 4, 2015

Laboratory validation of a sparse aperture image quality model

Proc. SPIE. 9617, Unconventional Imaging and Wavefront Sensing 2015

KEYWORDS: Optical transfer functions, Data modeling, Imaging systems, Spatial frequencies, Sensors, Image restoration, Wavefronts, Image quality, Modulation transfer functions, Systems modeling

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PROCEEDINGS ARTICLE | October 2, 2014

Chasing the TIRS ghosts: calibrating the Landsat 8 thermal bands

Proc. SPIE. 9218, Earth Observing Systems XIX

KEYWORDS: Telescopes, Statistical analysis, Landsat, Sensors, Calibration, Solids, Black bodies, Chemical elements, Stray light, Earth observing sensors

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The Thermal Infrared Sensor (TIRS) on board Landsat 8 has exhibited a number of anomalous characteristics that have made it difficult to calibrate. These anomalies include differences in the radiometric appearance across the blackbody pre- and post-launch, variations in the cross calibration ratios between detectors that overlap on adjacent arrays (resulting in banding) and bias errors in the absolute calibration that can change spatially/temporally. Several updates to the TIRS calibration procedures were made in the months after launch to attempt to mitigate the impact of these anomalies on flat fielding (cosmetic removal of banding and striping) and mean level bias correction. As a result, banding and striping variations have been reduced but not eliminated and residual bias errors in band 10 should be less than 2 degrees for most targets but can be significantly more in some cases and are often larger in band 11. These corrections have all been essentially ad hoc without understanding or properly accounting for the source of the anomalies, which were, at the time unknown. This paper addresses the procedures that have been undertaken to; better characterize the nature of these anomalies, attempt to identify the source(s) of the anomalies, quantify the phenomenon responsible for them, and develop correction procedures to more effectively remove the impacts on the radiometric products. Our current understanding points to all of the anomalies being the result of internal reflections of energy from outside the target detector’s field-of-view, and often outside the telescope field-of-view, onto the target detector. This paper discusses how various members of the Landsat calibration team discovered the clues that led to how; these “ghosts” were identified, they are now being characterized, and their impact can hopefully eventually be corrected. This includes use of lunar scans to generate initial maps of influence regions, use of long path overlap ratios to explore sources of change and use of variations in bias calculated from truth sites to quantify influences from the surround on absolute bias errors.

PROCEEDINGS ARTICLE | May 23, 2014

A model-based ELM for atmospheric correction over Case 2 water with Landsat 8

Proc. SPIE. 9111, Ocean Sensing and Monitoring VI

KEYWORDS: Near infrared, Short wave infrared radiation, Landsat, Sensors, Satellites, Reflectivity, Atmospheric corrections, Atmospheric modeling, Model-based design, Earth observing sensors

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PROCEEDINGS ARTICLE | September 27, 2013

Influence of polarization phenomenology on material discriminability using multi-view polarimetric imagery

Proc. SPIE. 8873, Polarization Science and Remote Sensing VI

KEYWORDS: Target detection, Detection and tracking algorithms, Polarization, Imaging systems, Scattering, Sensors, Remote sensing, Polarimetry, Image sensors, Atmospheric sensing

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PROCEEDINGS ARTICLE | June 3, 2013

Analyzing radiometric requirements for diurnal observations of coastal/oceanic waters from geostationary orbits

Proc. SPIE. 8724, Ocean Sensing and Monitoring V

KEYWORDS: Signal to noise ratio, Sensors, Water, Remote sensing, Reflectivity, Interference (communication), Atmospheric propagation, Signal detection, Atmospheric sensing, Atmospheric modeling

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SPIE Journal Paper | May 22, 2013

Increased potential to monitor water quality in the near-shore environment with Landsat’s next-generation satellite

Gerace, Aaron , Schott, John , Nevins, Robert

JRS Vol. 7 Issue 01

KEYWORDS: Sensors, Signal to noise ratio, Landsat, Environmental monitoring, Atmospheric modeling, Signal processing, Quantization, Environmental sensing, Data modeling, Earth observing sensors

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PROCEEDINGS ARTICLE | May 18, 2013

Initial validation of atmospheric compensation for a Landsat land surface temperature product

Proc. SPIE. 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX

KEYWORDS: Landsat, Sensors, Calibration, Satellites, Clouds, Satellite imaging, Earth's atmosphere, Earth observing sensors, Temperature metrology, Radiative transfer

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PROCEEDINGS ARTICLE | November 19, 2012

Simulation of the performance and image quality characteristics of the Landsat OLI and TIRS sensors using DIRSIG

Proc. SPIE. 8533, Sensors, Systems, and Next-Generation Satellites XVI

KEYWORDS: Infrared sensors, Landsat, Data modeling, Sensors, Calibration, Image quality, Image sensors, Neodymium, Device simulation, Earth observing sensors

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The Digital Imaging and Remote Sensing (DIRS) Image Generation (DIRSIG) model has been significantly upgraded to support the Landsat Data Continuity Mission (LDCM). The DIRSIG improvements simulate the LDCM Thermal Infrared Sensor (TIRS) and the Operational Land Imager (OLI) sensor’s characteristics in support of the NASA and USGS image quality assessment programs. These improvements allow for simulation of spacecraft orbits using standard NORAD two line element (TLE) orbital descriptors. Sensor improvements include individual detector element lines of site, relative spectral response (RSR), bias, gain, non-linear response, and noise. Using DIRSIG’s existing source-targetsensor radiative transfer, atmospheric propagation, scene simulation, and thermal models, simulated Landsat 8 imagery was generated. These tools were developed to enable assessment of design trades during instrument development and build, and evaluation of expected performance during instrument test, as test data is used to refine the modeled instrument performance. Current efforts are aimed at refining predicted performance models, simulating on-orbit calibration maneuvers and generation of data to test data processing and analysis algorithms. Initial studies are aimed at assessing the impact of RSR variation on banding and striping in both OLI and TIRS and the use of side slither (90° Yaw) as a possible method to characterize and potentially compensate for non-linearity effects. Ongoing work aimed at simulating targets to support image based registration of the OLI and TIRS instruments is also presented. In general, the use of advanced simulation and modeling tools to support instrument design trades, image quality prediction, on-orbit image quality assessment and operational trades is reviewed. The overall effort is designed to provide simulated imagery incorporating all aspects of the instrument acquisition physics and scene phenomenology in support of instrument developers, operators, and Earth observation scientists.

PROCEEDINGS ARTICLE | June 12, 2012

Cross-calibration of Landsat-7's visible-near-infrared bands with Terra-MODIS over dark waters

Proc. SPIE. 8372, Ocean Sensing and Monitoring IV

KEYWORDS: Near infrared, MODIS, Landsat, Sensors, Aerosols, Calibration, Reflectivity, Atmospheric physics, Atmospheric modeling, Earth observing sensors

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Since its launch, the Enhanced Thematic Mapper plus (ETM+) onboard Landsat-7 has been continuously monitored via different calibration techniques to ensure it maintains the science requirements for the demanding application areas. The majority of its applications, including agriculture and forestry, require a robust calibration for medium to high reflective targets. However, when imaging water resources, then the question becomes whether the calibration coefficients are valid for the dark end of the sensor's responsivity curve. Motivated by the Landsat Data Continuity Mission (LDCM) and its potential for providing long-term, robust water studies, in this effort, the calibration status of Landsat-7's visible bands are examined using a cross-calibration technique. The well-calibrated Terra-MODIS scenes of the past decade over relatively optically stable waters were chosen to evaluate Landsat-7's stability. Following the geo-registration, homogenous areas exhibiting near, identical atmospheric conditions were specified as regions of interest in the calibration sites representing a broad range of water-leaving signals. The differences in the relative spectral response functions of the two sensors were accounted for via a model-based technique. The cross-comparison showed that the calibration instability of Landsat-7's reflective bands lie well within its radiometric uncertainty. The slight calibration differences were found to be less than 1%, 0.5%, and 2.5% for the blue, green and the red bands obtained for the period when the Terra-MODIS has been radiometrically stable. The Landsat-7's NIR band, however, exhibits, on average, 6.3% higher signals than those of Terra-MODIS. The results indicate that although Landsat-7 is well calibrated, its calibration status should be quantified rigorously for water studies when physics-based methods are employed for the removal of atmospheric effects. The over-water characterization of Landsat satellites becomes more crucial when the LDCM becomes operational.

PROCEEDINGS ARTICLE | June 12, 2012

Over-water atmospheric correction for Landsat's new OLI sensor

Proc. SPIE. 8372, Ocean Sensing and Monitoring IV

KEYWORDS: Signal to noise ratio, Near infrared, Landsat, Data modeling, Sensors, Aerosols, Atmospheric corrections, Atmospheric sensing, Atmospheric modeling, Earth observing sensors

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PROCEEDINGS ARTICLE | May 24, 2012

Using DIRSIG to identify uniform sites and demonstrate the utility of the side-slither calibration technique for Landsat's new pushbroom instruments

Proc. SPIE. 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII

KEYWORDS: Infrared sensors, Landsat, Data modeling, Imaging systems, Sensors, Calibration, Image sensors, Digital imaging, Imaging arrays, Earth observing sensors

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PROCEEDINGS ARTICLE | September 14, 2011

Modeling the image performance of the Landsat Data Continuity Mission sensors

Proc. SPIE. 8153, Earth Observing Systems XVI

KEYWORDS: Infrared sensors, Landsat, Optical spheres, Data modeling, Imaging systems, Sensors, Calibration, Signal detection, Atmospheric modeling, Earth observing sensors

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PROCEEDINGS ARTICLE | May 20, 2011

Data-driven simulations of the Landsat Data Continuity Mission (LDCM) platform

Proc. SPIE. 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII

KEYWORDS: Infrared sensors, Landsat, Data modeling, Imaging systems, Sensors, Data archive systems, Digital imaging, Bridges, Space operations, Earth observing sensors

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The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are two new sensors being developed by the Landsat Data Continuity Mission (LDCM) that will extend over 35 years of archived Landsat data. In a departure from the whiskbroom design used by all previous generations of Landsat, the LDCM system will employ a pushbroom technology. Although the newly adopted modular array, pushbroom architecture has several advantages over the previous whiskbroom design, registration of the multi-spectral data products is a concern. In this paper, the Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool was used to simulate an LDCM collection, which gives the team access to data that would not otherwise be available prior to launch. The DIRSIG model was used to simulate the two-instrument LDCM payload in order to study the geometric and radiometric impacts of the sensor design on the proposed processing chain. The Lake Tahoe area located in eastern California was chosen for this work because of its dramatic change in elevation, which was ideal for studying the geometric effects of the new Landsat sensor design. Multi-modal datasets were used to create the Lake Tahoe site model for use in DIRSIG. National Elevation Dataset (NED) data were used to create the digital elevation map (DEM) required by DIRSIG, QuickBird data were used to identify different material classes in the scene, and ASTER and Hyperion spectral data were used to assign radiometric properties to those classes. In order to model a realistic Landsat orbit in these simulations, orbital parameters were obtained from a Landsat 7 two-line element set and propagated with the SGP4 orbital position model. Line-of-sight vectors defining how the individual detector elements of the OLI and TIRS instruments project through the optics were measured and provided by NASA. Additionally, the relative spectral response functions for the 9 bands of OLI and the 2 bands of TIRS were measured and provided by NASA. The instruments were offset on the virtual satellite and data recorders used to generate ephemeris data for downstream processing. Finally, potential platform jitter spectra were measured and provided by NASA and incorporated into the simulations. Simulated imagery generated by the model was incrementally provided to the rest of the LDCM team in a spiral development cycle to constantly refine the simulations.

PROCEEDINGS ARTICLE | May 5, 2011

Using thermal remote sensing as a tool for calibrating a hydrodynamic model in inland waters

Proc. SPIE. 8030, Ocean Sensing and Monitoring III

KEYWORDS: Landsat, Coastal modeling, Data modeling, Sensors, Calibration, Remote sensing, 3D modeling, Thermal modeling, Atmospheric modeling, Earth observing sensors

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SPIE Journal Paper | November 1, 2010

Assessing the impact of spectral and polarimetric data fusion via simulation to support multimodal sensor system design requirements

Flusche, Brian , Gartley, Michael , Schott, John

JRS Vol. 4 Issue 01

KEYWORDS: Polarimetry, Sensors, Detection and tracking algorithms, Signal to noise ratio, Target detection, Data fusion, Sun, Image analysis, Digital imaging, Image sensors

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SPIE Journal Paper | November 1, 2010

Spectro-polarimetric bidirectional reflectance distribution function determination of in-scene materials and its use in target detection applications

Bartlett, Brent , Gartley, Michael , Messinger, David , Salvaggio, Carl , Schott, John

JRS Vol. 4 Issue 01

KEYWORDS: Polarimetry, Reflectivity, Target detection, Bidirectional reflectance transmission function, Calibration, RGB color model, Stereolithography, Data modeling, Sensors, Hyperspectral target detection

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SPIE Journal Paper | October 1, 2010

Defining a process to fuse polarimetric and spectral data for target detection and explore the trade space via simulation

Flusche, Brian , Gartley, Michael , Schott, John

JRS Vol. 4 Issue 01

KEYWORDS: Polarimetry, Detection and tracking algorithms, Sensors, Target detection, Image fusion, Data fusion, Remote sensing, Image analysis, Atmospheric modeling, Image processing

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PROCEEDINGS ARTICLE | August 13, 2010

Exploiting spectral and polarimetric data fusion to enhance target detection performance

Proc. SPIE. 7812, Imaging Spectrometry XV

KEYWORDS: Target detection, Signal to noise ratio, Image fusion, Detection and tracking algorithms, Sensors, Polarimetry, Image analysis, Image sensors, Atmospheric modeling, Data fusion

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PROCEEDINGS ARTICLE | August 21, 2009

Landsat-7 and Landsat-5 thermal band calibration updates

Proc. SPIE. 7452, Earth Observing Systems XIV

KEYWORDS: Mirrors, Landsat, Calibration, Error analysis, Data acquisition, Data archive systems, Data centers, Camera shutters, Earth observing sensors, Temperature metrology

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Landsat-7 ETM+, launched in April 1999, and Landsat-5 TM, launched in 1984, both have a single thermal band. Both instruments' thermal band calibrations have been updated: ETM+ in 2001 for a pre-launch calibration error and TM in 2007 for data acquired since the current era of vicarious calibration has been in place (1999). This year, the vicarious calibration teams have made regular collects of very hot targets, and have been able to make use of archived buoy data to extend the TM calibration back in time. The new data has made it clear that both instruments require slight adjustments in their thermal calibration coefficients. These new coefficients will be generated and put into the operational processing system to remove the calibration errors. The JPL vicarious calibration team has long operated automated buoys on Lake Tahoe for the purpose of vicarious calibration. This year, the Salton Sea station came on line. Salton Sea, located in southern California, gets far hotter than Lake Tahoe. Vicarious calibration results of the Salton Sea for both instruments added to the understanding of a small gain error that the Tahoe data had suggested. With the Salton Sea data, an ETM+ gain error became statistically significant. Though it causes errors as large as 1.2K at high temperatures (35C), at more usual earth temperatures (4-20C) the calibration error is within the noise of the calibration methodology (+/-0.6K). With an ETM+ calibration update, the RMSE will be +/-0.6K for all temperatures. The RIT vicarious calibration team mined the archive of the NOAA National Data Buoy Center for sites on the Great Lakes and in the Atlantic Ocean where buoy data was regularly available between 1984 and 2007 and there were radiosonde data within close proximity to allow for atmospheric correction. Four Landsat scenes were chosen and the study made use of almost 200 separate acquisitions of these scenes. The technique was first tested with Landsat-7 data, and was shown to be as reliable as the standard RIT vicarious calibration methods. The TM calibration was largely unmonitored for most if it's lifetime. The buoy results suggest a lifetime error in gain and a change in the offset after 1997. The 2007 TM calibration update accounted for much of the offset error but was only implemented for data acquired after 1999. With the additional buoy data, the calibration will be corrected for the earlier time period and the result will be a consistent calibration to within +/-0.6K for the lifetime of the TM.

PROCEEDINGS ARTICLE | August 21, 2009

The increased potential for the Landsat Data Continuity Mission to contribute to case 2 water quality studies

Proc. SPIE. 7452, Earth Observing Systems XIV

KEYWORDS: Signal to noise ratio, Landsat, Scattering, Sensors, Light scattering, Signal processing, Quantization, Spatial resolution, Earth observing sensors, Absorption

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PROCEEDINGS ARTICLE | August 12, 2009

Spectro-polarimetric BRDF determination of objects using in-scene calibration materials for polarimetric imagers

Proc. SPIE. 7461, Polarization Science and Remote Sensing IV

KEYWORDS: Polarization, Imaging systems, Cameras, Sensors, Calibration, Reflectivity, Polarimetry, Bidirectional reflectance transmission function, Atmospheric modeling, RGB color model

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PROCEEDINGS ARTICLE | May 6, 2009

Segmented aperture space telescope modeling used for remote sensing and image utility analysis

Proc. SPIE. 7330, Sensors and Systems for Space Applications III

KEYWORDS: Signal to noise ratio, Near infrared, Mirrors, Radon, Detection and tracking algorithms, Sensors, Image segmentation, Motion detection, Image quality, Modulation transfer functions

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SPIE Press Book | March 30, 2009

Fundamentals of Polarimetric Remote Sensing

Schott, John

SPIE Journal Paper | February 1, 2009

Atmospheric compensation in the presence of clouds: an adaptive empirical line method (AELM) approach

Bartlett, Brent , Schott, John

JRS Vol. 3 Issue 01

KEYWORDS: Clouds, Reflectivity, Sensors, Cameras, 3D modeling, Atmospheric modeling, Calibration, Data modeling, Atmospheric sensing, Solar energy

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SPIE Journal Paper | September 1, 2008

Spin-image target detection algorithm applied to low density 3D point clouds

Foster, Michael , Schott, John , Messinger, David

JRS Vol. 2 Issue 01

KEYWORDS: Clouds, 3D acquisition, Sensors, Target detection, LIDAR, 3D modeling, Detection and tracking algorithms, Staring arrays, Remote sensing, Model-based design

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PROCEEDINGS ARTICLE | August 27, 2008

Micro-scale modeling of contaminant effects on surface optical properties

Proc. SPIE. 7086, Imaging Spectrometry XIII

KEYWORDS: Polarization, Reflection, Scattering, Sensors, Water, Particles, Reflectivity, 3D modeling, Bidirectional reflectance transmission function, Soil contamination

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PROCEEDINGS ARTICLE | April 15, 2008

Phase retrieval in sparse aperture systems with phase diversity: a trade space study

Proc. SPIE. 6958, Sensors and Systems for Space Applications II

KEYWORDS: Signal to noise ratio, Optical transfer functions, Point spread functions, Telescopes, Mirrors, Sensors, Remote sensing, Phase retrieval, Space telescopes, RGB color model

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PROCEEDINGS ARTICLE | March 31, 2008

Micro-scale surface and contaminate modeling for polarimetric signature prediction

Proc. SPIE. 6972, Polarization: Measurement, Analysis, and Remote Sensing VIII

KEYWORDS: Reflection, Scattering, Sensors, Reflectivity, Polarimetry, 3D modeling, Directed energy weapons, Refraction, Bidirectional reflectance transmission function, Radiometry

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PROCEEDINGS ARTICLE | September 26, 2007

Use of Lidar data to geometrically constrain radiance spaces for physics-based target detection

Proc. SPIE. 6661, Imaging Spectrometry XII

KEYWORDS: Target detection, Staring arrays, LIDAR, Sensors, Reflectivity, Clouds, 3D modeling, Atmospheric physics, Vector spaces, Atmospheric modeling

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Upon collecting hyperspectral image (HSI) data, several steps are usually required prior to comparing sensor data to a target reflectance spectrum of interest. A common practice is the application of an atmospheric compensation routine, which converts a spectral cube from the radiance domain to the reflectance domain. Such routines may prove to be problematic when predicting reflectance spectra for subpixel targets under varying illumination conditions. An alternative to atmospheric compensation is to employ physics-based forward models to predict the ways that a target spectrum might appear at the sensor. Instead of generating a single target radiance vector, a target vector space is created, which theoretically spans all possible target manifestations in the sensor radiance cube. Typically, a background vector space is also generated using in-scene radiance vectors that are significantly unlike the target space. Target detection then occurs in the radiance domain by comparing a scene pixel's radiance spectrum to the target and background vector spaces. One disadvantage of using such physics-based model approaches is that the complexity of the radiance model drives the span of the target vector space. It may be possible to optimize the volume of target spaces on a local basis by incorporating spatial information, as provided by a geo-registered, co-temporal topographical Lidar data set. Such data may serve to eliminate geometric ambiguity at any given location in a scene; thus, the local target vector space may be constrained relative to the global target space. In doing so, target detection performance may be improved. A description of the various image processing techniques used to constrain target vector spaces is presented, including estimation of shadows, ground plane orientation, skydome visibility, and pixel purity. Finally, target detection performance resulting from the constrained vector space approach is discussed.

PROCEEDINGS ARTICLE | September 13, 2007

Polarimetric scene modeling in the thermal infrared

Proc. SPIE. 6682, Polarization Science and Remote Sensing III

KEYWORDS: Thermography, Infrared imaging, Polarization, Cameras, Reflectivity, Polarizers, Polarimetry, Bidirectional reflectance transmission function, Infrared radiation, Thermal modeling

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PROCEEDINGS ARTICLE | September 12, 2007

Atmospheric inversion in the presence of clouds: an adaptive ELM approach

Proc. SPIE. 6661, Imaging Spectrometry XII

KEYWORDS: Hyperspectral imaging, Data modeling, Cameras, Sensors, Calibration, Reflectivity, Clouds, 3D modeling, Atmospheric sensing, Atmospheric modeling

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PROCEEDINGS ARTICLE | September 12, 2007

Synthetic data generation of high-resolution hyperspectral data using DIRSIG

Proc. SPIE. 6661, Imaging Spectrometry XII

KEYWORDS: Detection and tracking algorithms, Data modeling, Sensors, Calibration, Remote sensing, Reflectivity, Vegetation, Digital imaging, Algorithm development, Atmospheric modeling

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SPIE Journal Paper | July 1, 2007

Comparative evaluation of background characterization techniques for hyperspectral unstructured matched filter target detection

West, Jason , Messinger, David , Schott, John

JRS Vol. 1 Issue 01

KEYWORDS: Target detection, Sensors, Statistical analysis, Detection and tracking algorithms, Image segmentation, Target designation, Optical filters, Hyperspectral imaging, Mahalanobis distance, Roads

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Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters, which may be derived in many different fields, can be used to locate spectral targets by modeling the scene background as either structured (i.e., geometric, with endmembers or basis vectors) or unstructured (i.e., stochastic, with a mean and covariance matrix). In unstructured techniques, various methods of calculating the background covariance matrix have been developed, each involving either the removal of target signatures from the background model or the appropriate choice of a subset of the image to be used as training data based on spatial or spectral characteristics. The objective of these methods is to derive a background which matches the source of mixture interference for the detection of sub-pixel targets, or matches the source of false alarms in the scene for the detection of fully resolved targets. In addition, these techniques may increase the multivariate normality of the data from which the background is characterized, thus increasing adherence to the normality assumption inherent in the matched filter and ultimately improving target detection results. Such techniques for improved background characterization are widely practiced but not well understood or documented. This work describes in detail several methods for unstructured background characterization, both new methods and those taken from the literature. A comparison of methods across a common set of targets is presented. Training data are chosen either spatially, spectrally, or both prior to estimation of the background statistics. Evaluation of the methods indicates that consistent improvements over scene-wide statistics can be achieved through such spatial or spectral subsetting of the image data. However, results are not conclusive as to the overall "best" method for all targets in the test set.

PROCEEDINGS ARTICLE | October 30, 2006

Landsat TM and ETM+ thermal band calibration

Proc. SPIE. 6296, Earth Observing Systems XI

KEYWORDS: Landsat, Sensors, Calibration, Databases, Satellites, Data acquisition, Black bodies, Camera shutters, Earth observing sensors, Stanford Linear Collider

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Landsat-5 Thematic Mapper (TM) has been imaging the Earth since March 1984 and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) was added to the series of Landsat instruments in April 1999. The stability and calibration of the ETM+ has been monitored extensively since launch. Though not monitored for many years, TM now has a similar system in place to monitor stability and calibration. University teams have been evaluating the on-board calibration of the instruments through ground-based measurements since 1999. This paper considers the calibration efforts for the thermal band, Band 6, of both the Landsat-5 and Landsat-7 instruments. Initial calibration results for the Landsat-7 ETM+ thermal band found a bias error which was corrected through changes in the processing systems in late 2000. Recent results are suggesting a calibration error in gain, apparent with high temperature targets. For typical earth temperature targets, from about 5-20C, the gain error is small enough to be within the noise of the vicarious calibration process. However, for very high temperature targets (greater then 35C), Landsat-7 appears to be predicting several degrees too low. Questions remain on whether the change happened suddenly or is varying slowly, so the team will wait for another collection season before making any updates to the calibration. The calibration efforts for Landsat-5 TM considers only data collected since 1999, though there are efforts underway to extend the calibration history prior to the Landsat-7 launch. The latest data suggests that the Landsat-5 thermal band has a bias error of about 0.65K too low since 1999. Studies early in the life of Landsat-5 show that the instrument was calibrated within the error of the calibration process. It is impossible to tell, at this point, when or how the change in bias may have occurred. A correction will be calculated and implemented in the US processing system in 2006 for data acquired since April 1999.

PROCEEDINGS ARTICLE | September 1, 2006

Ground truth-based variability analysis of atmospheric inversion in the presence of clouds

Proc. SPIE. 6301, Atmospheric and Environmental Remote Sensing Data Processing and Utilization II: Perspective on Calibration/Validation Initiatives and Strategies

KEYWORDS: Sensors, Calibration, Remote sensing, Photons, Reflectivity, Clouds, Atmospheric corrections, Atmospheric sensing, Atmospheric particles, Atmospheric modeling

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PROCEEDINGS ARTICLE | September 1, 2006

The incorporation of atmospheric variability in hyperspectral synthetic scene simulation

Proc. SPIE. 6302, Imaging Spectrometry XI

KEYWORDS: Data modeling, Sensors, Aerosols, Databases, Digital imaging, Atmospheric sensing, Atmospheric particles, Atmospheric modeling, Atmospheric optics, RGB color model

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PROCEEDINGS ARTICLE | May 19, 2006

First-principles based LIDAR simulation environment for scenes with participating mediums

Proc. SPIE. 6214, Laser Radar Technology and Applications XI

KEYWORDS: Statistical analysis, Scattering, LIDAR, Sensors, Laser scattering, Time correlated photon counting, Multiple scattering, Atmospheric modeling, Systems modeling, Absorption

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PROCEEDINGS ARTICLE | May 12, 2006

A flexible hyperspectral simulation tool for complex littoral environments

Proc. SPIE. 6204, Photonics for Port and Harbor Security II

KEYWORDS: Optical spheres, Submerged target modeling, Data modeling, Optical properties, Scattering, Sensors, Monte Carlo methods, Atmospheric propagation, Photon transport, Hyperspectral simulation

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PROCEEDINGS ARTICLE | May 4, 2006

Land surface temperature and emissivity retrieval from thermal infrared hyperspectral imagery

Proc. SPIE. 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII

KEYWORDS: Thermography, Hyperspectral imaging, Sensors, Atmospheric physics, Black bodies, Transmittance, Infrared radiation, Optimization (mathematics), Atmospheric sensing, Atmospheric modeling

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PROCEEDINGS ARTICLE | August 22, 2005

Validation of a web-based atmospheric correction tool for single thermal band instruments

Proc. SPIE. 5882, Earth Observing Systems X

KEYWORDS: Thermography, Landsat, Sensors, Calibration, Satellites, Atmospheric corrections, Atmospheric modeling, Earth observing sensors, Temperature metrology, Radiative transfer

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PROCEEDINGS ARTICLE | June 1, 2005

Performance comparison of hyperspectral target detection algorithms in altitude varying scenes