Constant False Alarm Rate in Fire Detection for MODIS Data
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Transcript of Constant False Alarm Rate in Fire Detection for MODIS Data
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Constant False Alarm Rate in Fire Detection for MODIS
Data
Maurizio di BisceglieRoberto Episcopo
Lilli GaldiSilvia Ullo
Università del Sannio - Benevento - Italy
dbmeeting - Benevento 3 - 6 october 2005
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MODIS active fire algorithms, based on tests with absolute and adaptive thresholds, do not guarantee the control of the false alarm rate.
A Constant False Alarm Rate (CFAR) could be highly desirable and is a performance prerequisite in a changeable environment.
From the radar context we draw the idea of designing a CFAR algorithm for detecting thermal anomalies in 4μm MODIS channel.
Motivation and purpose
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Design of the CFAR detector
Validation of the statistical model
Algorithm description
Experimental results
Outline
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The concept of CFAR by an example
Suppose X is a Gaussian rv
background-only hypothesis
cell under test
adaptive threshold
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➡ The distribution of the data is non Gaussian
➡ The cells for estimating the background may contain thermal anomalies and this cause overestimation of the adaptive threshold
Real scenario
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is constant if
andare proper estimators from
theordered sample
depends on
and
location parameter scale parameter
standard variate with and
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Ranking preserves the LS property
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Scheme of the CFAR detector
System outline of the CFAR algorithm
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Statistical analysis
with a log-transformation becomes LS
estimation of the three parameters for statistical validation of real data
The validation of the model has been carried out evaluating a distributional distance between the theoretical and the empirical CDFs
Hypothesis model for 4μm MODIS brightness temperature: 3-parameter Weibull
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Parameter estimation algorithm
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Test area
Terra/MODIS true color, July 19th 2004, Campania region, Southern Italy
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Cramer-Von Mises distance
Distance between theoretical and empirical CDFs of 4μm MODIS brightness temperature
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Cumulative Distributions
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Sketch of fire detection algorithm
Preliminary processing
Window selection/sizing
Logarithm/ranking/censoring of data
Parameter estimation/threshold setting
Detection
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Preliminary processing
NASA-DAAC L0, L1 calibrations and geocoding
NASA-DAAC Land-See mask MOD 03
NASA-DAAC Cloud Mask MOD 35 (Modified)
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Window selection/sizing
Statistically homogeneous region
Constant number of cells inside the window (256 for this test case)
Initial partition into 16x16 square windows
If valid data < 256 → progressive enlargement until 256 valid data are found
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Data transformation
Subtraction of estimated δ for compatibility with a biparametric Weibull distribution
Log-transform for compatibility with a Location-Scale distribution
Sorting and censoring for discarding a given number of outliers that may correspond to thermal anomalies (censoring depths = 0, 4, 8 for this test case)
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Parameter and threshold estimation
Best Linear Unbiased estimation of background parameters to guarantee the CFAR property
Monte Carlo estimation of threshold multiplier as a function of the number of samples, the censoring depth and the desired rate of false alarm
Threshold setting
and
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Thermal anomalies detection
Results of detection on 4μm channel data with a censoring of 8 samples and Pfa=10-5
CFAR detection
MOD 14 detection
350
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[K]
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Future developments
Use of multiple bands for thermal anomalies detection
Checking distribution for a combination of channels
Refinement of the cloud detection algorithm
More sophisticated criterion of window selection for better background estimation