The process of automatically masking objects from complex backgrounds is extremely beneficial when trying to utilize those objects for computer vision research, such as object detection, autonomous driving, pedestrian tracking, etc. Therefore, a robust method of segmentation is imperative towards ongoing research between the Digital Imaging and Remote Sensing Laboratory at the Rochester Institute of Technology and the Savannah River National Laboratory directed at the volume estimation of condense water vapor plumes emanating from mechanical draft cooling towers. Instance segmentation was performed on a custom data set consisting of RGB imagery with the Matterport Mask R-CNN implementation,1 where condensed water vapor plumes were masked out from mixed backgrounds for the purpose of 3D reconstruction and volume estimation. This multi-class Mask R-CNN was trained to detect cooling tower structure and plumes with and without data augmentation to study the effects on a preliminary data set, in addition to a model trained with a single plume class. The average precision and intersection over union metrics across all models were shown to not be statistically different. While each model is capable of detecting and segmenting plumes in the preliminary data set, all models essentially perform the task with the same efficacy. This indicates some level of bias in the preliminary data set, demonstrating the need for more variance in the form of additional annotated imagery. The single plume class model tested within 7% for mAP, AP, and IoU when compared to the other two models, demonstrating the ability of Mask R-CNN to detect and segment these dynamically-changing plumes without any spatial dependence on the stationary cooling tower structure. This ongoing research includes a long-term data collection campaign where imagery of condensed water vapor plumes will be continuously gathered over an 18-month period so as to include imagery examples under many different meteorological and environmental conditions, seasonal variations, and illumination changes that will occur over an annual cycle. Including this data in future training of the Mask R-CNN implementation is expected to reduce any bias that may exist in the current data set.
The Digital Imaging and Remote Sensing Laboratory (DIRS) at the Rochester Institute of Technology, along
with the Savannah River National Laboratory is investigating passive methods to quantify vehicle loading.
The research described in this paper investigates multiple vehicle indicators including brake temperature, tire
temperature, engine temperature, acceleration and deceleration rates, engine acoustics, suspension response, tire
deformation and vibrational response. Our investigation into these variables includes building and implementing a
sensing system for data collection as well as multiple full-scale vehicle tests. The sensing system includes; infrared
video cameras, triaxial accelerometers, microphones, video cameras and thermocouples. The full scale testing
includes both a medium size dump truck and a tractor-trailer truck on closed courses with loads spanning the
full range of the vehicle's capacity. Statistical analysis of the collected data is used to determine the effectiveness
of each of the indicators for characterizing the weight of a vehicle. The final sensing system will monitor multiple
load indicators and combine the results to achieve a more accurate measurement than any of the indicators could
provide alone.
The Rochester Institute of Technology (RIT) collected visible, SWIR, MWIR and LWIR imagery of the Midland
(Michigan) Cogeneration Ventures Plant from aircraft during the winter of 2008 - 2009. RIT also made ground-based
measurements of lake water and ice temperatures, ice thickness and atmospheric variables. The Savannah River National
Laboratory (SRNL) used the data collected by RIT and a 3-D hydrodynamic code to simulate the Midland cooling lake.
The hydrodynamic code was able to reproduce the time distribution of ice coverage on the lake during the entire winter.
The simulations and data show that the amount of ice coverage is almost linearly proportional to the rate at which heat is
injected into the lake (Q). Very rapid melting of ice occurs when strong winds accelerate the movement of warm water
underneath the ice. A snow layer on top of the ice acts as an insulator and decreases the rate of heat loss from the water
below the ice to the atmosphere above. The simulated ice cover on the lake was not highly sensitive to the thickness of
the snow layer. The simplicity of the relationship between ice cover and Q and the weak responses of ice cover to snow depth over the ice are probably attributable to the negative feedback loop that exists between ice cover and heat loss to the atmosphere.
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.
The Savannah River National Laboratory (SRNL) collected thermal imagery and ground truth data at two commercial
power plant cooling lakes to investigate the applicability of laboratory empirical correlations between surface heat flux
and wind speed, and statistics derived from thermal imagery. SRNL demonstrated in a previous paper [1] that a linear
relationship exists between the standard deviation of image temperature and surface heat flux. In this paper, SRNL will
show that the skewness of the temperature distribution derived from cooling lake thermal images correlates with
instantaneous wind speed measured at the same location. SRNL collected thermal imagery, surface meteorology and
water temperatures from helicopters and boats at the Comanche Peak and H. B. Robinson nuclear power plant cooling
lakes. SRNL found that decreasing skewness correlated with increasing wind speed, as was the case for the laboratory
experiments. Simple linear and orthogonal regression models both explained about 50% of the variance in the skewness
- wind speed plots. A nonlinear (logistic) regression model produced a better fit to the data, apparently because the
thermal convection and resulting skewness are related to wind speed in a highly nonlinear way in nearly calm and in
windy conditions.
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.
Determining the internal temperature of a mechanical draft cooling tower (MDCT) from remotely-sensed thermal
imagery is important for many applications that provide input to energy-related process models. The problem
of determining the temperature of a MDCT is unique due to the geometry of the tower and due to the exhausted
water vapor plume. The radiance leaving the tower is dependent on the optical and thermal properties of the
tower materials (i.e., emissivity, BRDF, temperature, etc.) and also the internal geometry of the tower. The
tower radiance is then propagated through the exhaust plume and through the atmosphere to arrive at the sensor.
The expelled effluent from the tower consists of a warm plume with a higher water vapor concentration than
the ambient atmosphere. Given that a thermal image has been atmospherically compensated, the remaining
sources of error in extracted tower temperature due to the exhausted plume and the tower geometry must be
accounted for. A temperature correction factor due to these error sources will be derived through the use of
three-dimensional radiometric modeling. A range of values for each important parameter are modeled to create
a target space (i.e., look-up table) that predicts the internal MDCT temperature for every combination of
parameter values. This LUT, along with user knowledge of the scene, provides a means to convert the imagederived
apparent temperature into the estimated absolute temperature of a MDCT. Preliminary results indicate
that temperature error corrections of approximately 1 - 9 Kelvin can be achieved with the range of MDCT
parameters encompassed by the LUT.
The atmosphere is a critical factor in remote sensing. Radiance from a target must pass through the air column
to reach the sensor. The atmosphere alters the radiance reaching the sensor by attenuating the radiance from
the target via scattering and absorption and by introducing an upwelling radiance. In the thermal infrared,
these effects will introduce errors in the derived apparent temperature of the target if not properly accounted
for. The temperature error is defined as the difference between the target leaving apparent temperature and
observed apparent temperature. The effects of the atmosphere must be understood in order to develop methods
to compensate for this error. Different atmospheric components will affect the radiation passing through it in
different ways. Certain components may be more important than others depending on the remote sensing application.
The authors are interested in determining the actual temperature of the superstructure that composes
a mechanical draft cooling tower (MDCT), hence water vapor is the primary constituent of concern. The tower
generates a localized water vapor plume located between the target and sensor. The MODTRAN radiative
transfer code is used to model the effects of a localized exhaust plume from a MDCT in the longwave infrared.
The air temperature and dew point depression of the plume and the thickness of the plume are varied to observe
the effect on the apparent temperature error. In addition, the general atmospheric conditions are varied between
two standard MODTRAN atmospheres to study any effect that ambient conditions have on the apparent temperature
error. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) modeling tool is used to
simulate the radiance reaching a thermal sensor from a target after passing through the water vapor plume. The
DIRSIG results are validated against the MODTRAN results. This study shows that temperature errors of as
much as one Kelvin can be attributed to the presence of a localized water vapor plume.
Power plant-heated lakes are characterized by a temperature gradient in the thermal plume originating at the discharge of
the power plant and terminating at the water intake. The maximum water temperature discharged by the power plant into
the lake depends on the power generated at the facility and environmental regulations on the temperature of the lake.
Besides the observed thermal plume, cloud-like thermal cells (convection cell elements) are also observed on the water
surface. The size, shape and temperature of the convection cell elements depends on several parameters such as the lake water temperature, wind speed, surfactants and the depth of the thermocline. The Savannah River National Laboratory (SRNL) and Clemson University are collaborating to determine the applicability of laboratory empirical correlations between surface heat flux and thermal convection intensity. Laboratory experiments at Clemson University have demonstrated a simple relationship between the surface heat flux and the standard deviation of temperature fluctuations. Similar results were observed in the aerial thermal imagery SRNL collected at different locations along the thermal plume and at different elevations. SRNL will present evidence that the results at Clemson University are applicable to cooling lakes.
Laboratory experiments show a linear relationship between the total heat flux from a water surface to air and the
standard deviation of the surface temperature field, σ, derived from thermal images of the water surface over a range of
heat fluxes from 400 to 1800 Wm-2. Thermal imagery and surface data were collected at two power plant cooling lakes
to determine if the laboratory relationship between heat flux and σ exists in large heated bodies of water. The heat fluxes
computed from the cooling lake data range from 200 to 1400 Wm-2. The linear relationship between σ and Q is evident
in the cooling lake data, but it is necessary to apply band pass filtering to the thermal imagery to remove camera artifacts
and non-convective thermal gradients. The correlation between σ and Q is improved if a correction to the measured σ is
made that accounts for wind speed effects on the thermal convection. Based on more than a thousand cooling lake
images, the correlation coefficients between σ and Q ranged from about 0.8 to 0.9.
KEYWORDS: Bidirectional reflectance transmission function, Reflection, Reflectivity, Process modeling, Sensors, Black bodies, Infrared imaging, Solid modeling, Digital imaging, 3D modeling
Determining the temperature of an internal surface within cavernous targets, such as the interior wall of a mechanical draft cooling tower, from remotely sensed imagery is important for many surveillance applications that provide input to process models. The surface leaving radiance from an observed target is a combination of the self-emitted radiance and the reflected background radiance. The self-emitted radiance component is a function of the temperature-dependent blackbody radiation and the view-dependent directional emissivity. The reflected background radiance component depends on the bidirectional reflectance distribution function (BRDF) of the surface, the incident radiance from surrounding sources, and the BRDF for each of these background sources. Inside a cavity, the background radiance emanating from any of the multiple internal surfaces will be a combination of the self-emitted and reflected energy from the other internal surfaces as well as the downwelling sky radiance. This scenario provides for a complex radiometric inversion problem in order to arrive at the absolute temperature of any of these internal surfaces. The cavernous target has often been assumed to be a blackbody, but in field experiments it has been determined that this assumption does not always provide an accurate surface temperature. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) modeling tool is being used to represent a cavity target. The model demonstrates the dependence of the radiance reaching the sensor on the emissivity of the internal surfaces and the multiple internal interactions between all the surfaces that make up the overall target. The cavity model is extended to a detailed model of a mechanical draft cooling tower. The predictions of derived temperature from this model are compared to those derived from actual infrared imagery collected with a helicopter-based broadband infrared imaging system collected over an operating tower located at the Savannah River National Laboratory site.
Temperatures of the water surface of a cold, mid-latitude lake and the tropical Pacific Ocean were determined from MTI images and from in situ concurrent measurements. In situ measurements were obtained at the time of the MTI image with a floating, anchored platform, which measured the surface and bulk water temperatures and relevant meteorological variables, and also from a boat moving across the target area. Atmospheric profiles were obtained from concurrent radiosonde soundings. Radiances at the satellite were calculated with the Modtran radiative transfer model. The MTI infrared radiances were within 1% of the calculated values at the Pacific Ocean site but were 1-2% different over the mid-latitude lake.
MTI images of thermal discharge from three power plants are analyzed in this paper with the aid of a 3_D hydrodynamic code. The power plants use different methods to dissipate waste heat in the environment: a cooling lake at Comanche Peak, ocean discharge at Pilgrim and cooling canals at Turkey Point. This paper show s that it is possible to reproduce the temperature distributions captured in MTI imagery with accurate code inputs, but the key parameters change from site to site. Wind direction and speed are the most important parameters at Pilgrim, whereas air temperatures are most important at Comanche Peak and Turkey Point. This paper also shows how the combination of high- resolution thermal imagery and hydrodynamic simulation lead to better understanding of the mechanisms by which waste heat is dissipated in the environment.
Natural bodies of water have several advantages as IR calibration targets in remote sensing. Among these are availability, homogeneity, and accurate knowledge of emissivity. A portable, low-cost, floating apparatus is described for calibration of remote IR sensors to within 0.15 C. The apparatus measures the surface and bulk water temperature as well as the wind speed, direction, temperature, and relative humidity. The apparatus collects data automatically and can be deployed for up to 24 hours. The sources of uncertainty, including the effects of skin temperature and waves are discussed. Data from several field campaigns to calibrate IR bands of DOE's Multi-Spectral Thermal Imager are described along with estimates of error.
The Savannah River Technology Center (SRTC) selected 13 sites across the continental US and one site in the western Pacific to serve as the primary or core site for collection of ground truth data for validation of MTI science algorithms. Imagery and ground truth data from several of these sites are presented in this paper. These sites are the Comanche Peak, Pilgrim and Turkey Point power plants, Ivanpah playas, Crater Lake, Stennis Space Center and the Tropical Western Pacific ARM site on the island of Nauru. Ground truth data includes water temperatures (bulk and skin), radiometric data, meteorological data and plant operating data. The organizations that manage these sites assist SRTC with its ground truth data collections and also give the MTI project a variety of ground truth measurements that they make for their own purposes. Collectively, the ground truth data from the 14 core sites constitute a comprehensive database for science algorithm validation.
KEYWORDS: Temperature metrology, Skin, Astatine, Solar radiation models, Data modeling, 3D modeling, Thermography, Atmospheric modeling, Radiometry, Wind energy
The Savannah River Technology Center (SRTC) measured water skin temperatures at four of the Multi-spectral Thermal Imager (MTI) core sites. The depression of the skin temperature relative to the bulk water temperature ((Delta) T) a few centimeters below the surface is a complex function of the weather conditions, turbulent mixing in the water and the bulk water temperature. Observed skin temperature depressions range from near zero to more than 1.0 degree(s)C. Skin temperature depressions tend to be larger when the bulk water temperature is high, but large depressions were also observed in cool bodies of water in calm conditions at night. We compared (Delta) T predictions from three models (SRTC, Schlussel and Wick) against measured (Delta) T's from 15 data sets taken at the MTI core sites. The SRTC and Wick models performed somewhat better than the Schlussel model, with RMSE and average absolute errors of about 0.2 degree(s)C, relative to 0.4 degree(s)C for the Schlussel model. The average observed (Delta) T for all 15 databases was -0.7 degree(s)C.
The Savannah River Technology Center (SRTC) is currently calibrating the Multispectral Thermal Imager (MTI) satellite sponsored by the Department of Energy. The MTI imager is a research and development project with 15 wavebands in the visible, near-infrared, short-wave infrared, mid-wave infrared and long-wave infrared spectral regions. A plethora of targets with known temperatures such as power plant heated lakes, volcano lava vents, desert playas and aluminized Mylar tarps are being used in the validation of the five thermal bands of the MTI satellite. SRTC efforts in the production of cold targets with aluminized Mylar tarps will be described. Visible and thermal imagery and wavelength dependent radiance measurements of the calibration targets will be presented.
Remote sensing temperature measurements of water bodies is complicated by the temperature differences between the true surface or `skin' water and the bulk water below. Weather conditions control the reduction of the skin temperature relative to the bulk water temperature. Typical skin temperature depressions range from a few tenths of a degree Celsius to more than one degree. In this research project, the Savannah River Technology Center used aerial thermography and surface-based meteorological and water temperature measurements to study a power plant cooling lake in South Carolina. Skin and bulk water temperatures were measured simultaneously for imagery calibration and to product a database for modeling of skin temperature depressions as a function of weather and bulk water temperatures. This paper will present imagery that illustrates how the skin temperature depression was affected by different conditions in several locations on the lake and will present skin temperature modeling results.
Sandia National Laboratories (SNL), Los Alamos National Laboratory (LANL) and the Savannah River Technology Center (SRTC) have developed a diverse group of algorithms for processing and analyzing the data that will be collected by the Multispectral Thermal Imager (MTI) after launch late in 1999. Each of these algorithms must be verified by comparison to independent surface and atmospheric measurements. SRTC has selected 13 sites in the continental U.S. for ground truth data collections. These sites include a high altitude cold water target (Crater Lake), cooling lakes and towers in the warm, humid southeastern U.S., Department of Energy (DOE) climate research sites, the NASA Stennis satellite Validation and Verification (V&V) target array, waste sites at the Savannah River Site, mining sites in the Four Corners area and dry lake beds in Nevada. SRTC has established mutually beneficial relationships with the organizations that manage these sites to make use of their operating and research data and to install additional instrumentation needed for MTI algorithm V&V.
The Multispectral Thermal Imager (MTI) is a research and development project sponsored by the Department of Energy and executed by Sandia and Los Alamos National Laboratories and the Savannah River Technology Center. Other participants include the U.S. Air Force, universities, and many industrial partners. The MTI mission is to demonstrate the efficacy of highly accurate multispectral imaging for passive characterization of industrial facilities and related environmental impacts from space. MTI provides simultaneous data for atmospheric characterization at high spatial resolution. Additionally, MTI has applications to environmental monitoring and other civilian applications. The mission is based in end-to-end modeling of targets, signatures, atmospheric effects, the space sensor, and analysis techniques to form a balanced, self-consistent mission. The MTI satellite nears completion, and is scheduled for launch in late 1999. This paper describes the MTI mission, development of desired system attributes, some trade studies, schedule, and overall plans for data acquisition and analysis. This effort drives the sophisticated payload and advanced calibration systems, which are the overall subject of the first session at this conference, as well as the data processing and some of the analysis tools that will be described in the second segment.
Many remote sensing applications rely on imaging spectrometry. Here we use imaging spectrometry for thermal and multispectral signatures measured from a satellite platform enhanced with a combination of accurate calibrations and on-board data for correcting atmospheric distortions. Our approach is supported by physics-based end- to-end modeling and analysis, which permits a cost-effective balance between various hardware and software aspects.
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