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|Title: ||A QUANTITATIVE THERMAL IMAGING TECHNIQUE TO EXTRACT A CROSS-STREAM SURFACE VELOCITY PROFILE FROM A FLOWING BODY OF WATER|
|Authors: ||Helmle, Chad S.|
|Keywords: ||quantitative imaging|
|Issue Date: ||9-May-2005|
|Abstract: ||The United States Geological Survey (USGS) is responsible for monitoring river flow rates at over 7,000 locations across the United States. This operation is expensive, inefficient, and often dangerous, as USGS personnel must deploy direct in-the-field measurement equipment to obtain the required flow data during storm events. An affordable, remote, non-contact sensor that is capable of determining river flow rates under a variety of flow environments is, therefore, highly desired by the USGS and other agencies worldwide charged with the task of water flow monitoring and management.
A technique is presented in which a cross-stream surface velocity profile is extracted from a series of thermal infrared images of a flowing water surface. Analytical methods and algorithms are borrowed from Quantitative Imaging (QI) fields such as Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV), and are adapted for use on the thermographic image set. Monte Carlo Simulations are used to compare several iterative improvements to the initial correlation-based displacement algorithm. Particular emphasis is placed upon extracting reliable results from images with a very low signal-to-noise ratio (SNR) typical of the image type recorded by an inexpensive thermal imaging system in the nearly uniform temperature environment of interest.
Laboratory experiments are used to verify the capacity of the altered displacement algorithm to extract cross-stream velocity profiles from thermal images recorded from above an open-channel flume. Several cases ranging from high to low SNR are studied. The displacement algorithm?s analysis of the high SNR data sets provides a velocity profile that agrees with the profile measured by an Acoustic Doppler Velocimeter (ADV). Displacement results for the medium and low SNR cases required further processing after the initial displacement algorithm analysis. A qualitative analysis of the post-processed data reveals that a deterministic signal can be extracted from such noisy image sets, and further refinements of the displacement algorithms to accomplish these tasks are possible.|
|Description: ||Committee Chairperson: Prof. Edwin A. Cowen
Committee Member: Prof. Tammo Steenhuis|
|Appears in Collections:||Cornell Theses and Dissertations|
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