Threshold Performance Research Articles

Validation and potential forecast use of a debris-flow rainfall threshold calibrated with the Backward Dynamical Approach

The aim of this work is to propose a way of validating a rainfall threshold for stony debris flow calibrated with the Backward Dynamical Approach (BDA), a physical-based approach that combines the debris flows' forcing and dynamics. Given the assumptions underlying the BDA, two different validation approaches are developed and then applied to a suitable study area for which a calibrated BDA threshold and a dataset related to a calibration-independent reference period are available. The first approach assesses the reliability of the threshold by applying the BDA to compute the rainfall conditions related to the debris flows that occurred. Instead, the second approach, thanks to the introduction of the concept of “potential debris flows”, considers all rainfall events, regardless of whether or not they are associated with a debris flow, providing a complete overview of the threshold reliability. In both approaches, the threshold's performances are quantified by employing the contingency table and skill scores. The results of the validations applied to the study area threshold highlight its satisfactory reliability. Moreover, the introduction of the potential debris flows concept has cleared the way to apply a BDA-based threshold in a predictive framework and to roughly estimate the solid volumes we may expect if a debris flow occurs.

TOD Performance Model for Staring Thermal Imager with Machine Vision

A triangle orientation discrimination threshold(TOD) performance theoretical model is derived for the staring thermal imager based on machine vision. Specifically, how to obtain the TOD curve based on machine vision is briefly described. The spatial frequency distribution of the triangle test pattern is first determined. The transform and response characteristics of the non-periodic triangle pattern and its background clutter through machine vision-based thermal imager are analyzed. The three-dimensional matched filter is adopted to characterize quantitatively the spatial and temporal integration of image enhancement algorithms to the output triangle pattern signal, various noise components and background clutter, and the signal-to-interference ratio (SIR) of the triangle pattern output image is derived for the staring thermal imager based on machine vision. Then, the TOD performance theoretical model is established by assuming that the output SIR is equal to the threshold SIR75% determined by the discrimination criteria of machine vision. Preliminary simulation and experimental results show that this theoretical model can give reasonable prediction of the TOD performance curve for staring thermal imagers based on machine vision.

TOD performance theoretical model for scanning infrared imagers

Since the triangle orientation discrimination threshold (TOD) measure was proposed, good progress has been made in some aspects, such as experimental validation, field performance, and TOD sensor performance model. However, what is still missing is a TOD curve prediction model similar to the minimum resolvable temperature difference (MRTD) equation. In this paper, an equation-based TOD performance model was first developed for scanning thermal imagers. Specifically, the spatial distribution of the triangle standard test pattern is described quantitatively. The resolution transfer characteristic of the scanning infrared imagers is modeled with linear system theory, and system noise is characterized by introducing a three-dimensional noise model. The modulation effect of overall subsystems of scanning thermal imager on the non-periodic standard triangle pattern is analyzed. The matched filter idea is adopted to characterize quantitatively the spatial–temporal integration of the human visual system to signal and various noise components over the triangle pattern area, and the perceived signal-to-noise ratio for the scanning infrared imager is derived. From this, a TOD performance theoretical model is established. Comparisons with experimental results show that this theoretical model gives reasonable prediction of the TOD performance curve for scanning thermal imagers. Although more tests and modifications are required, these preliminary results suggest that this model can be developed into a model that predicts the TOD for all kinds of sensors.

TOD Performance Theoretical Model for Staring Thermal Imagers

The TOD (triangle orientation discrimination threshold) metric, as a promising sensor performance measure, is suitable for all types of thermal imaging systems and corresponds to the field performance very well. Since this method was proposed, a great progress has been made in some aspects, such as experimental validation, field performance, and TOD sensor performance model. However, what is still missing is a TOD curve prediction model similar to MRTD (minimum resolvable temperature difference) equation. Here, an equation-based TOD performance model was first developed focusing on staring thermal imagers. Specifically, spatial distribution of the triangle standard test pattern is described quantitatively. Resolution transfer characteristic of the staring infrared imagers is modeled with linear system theory, and system noise is characterized with the introduction of three-dimensional noise model. The modulation effect of overall subsystem of staring thermal imager on the non-periodic triangle standard pattern is analyzed. The matched filter idea is adopted to characterize quantitatively the spatial integration of the eye to signal and various noise components over the triangle pattern area, and the perceived signal-to-noise ratio of the staring thermal imager is derived. Then, the TOD performance theoretical model is established. Comparing with experimental results, this theoretical model gives reasonable prediction of the TOD performance curve of staring thermal imagers. Although more tests and modifications are required, these preliminary results suggest that this model can be developed into a model that predicts the TOD for all kinds of sensors.