Constant False Alarm Rate in Fire Detection for MODIS Data
description
Transcript of Constant False Alarm Rate in Fire Detection for MODIS Data
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
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
Design of the CFAR detector
Validation of the statistical model
Algorithm description
Experimental results
Outline
The concept of CFAR by an example
Suppose X is a Gaussian rv
background-only hypothesis
cell under test
adaptive threshold
➡ 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
is constant if
andare proper estimators from
theordered sample
depends on
and
location parameter scale parameter
standard variate with and
Ranking preserves the LS property
Scheme of the CFAR detector
System outline of the CFAR algorithm
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
Parameter estimation algorithm
Test area
Terra/MODIS true color, July 19th 2004, Campania region, Southern Italy
Cramer-Von Mises distance
Distance between theoretical and empirical CDFs of 4μm MODIS brightness temperature
Cumulative Distributions
Sketch of fire detection algorithm
Preliminary processing
Window selection/sizing
Logarithm/ranking/censoring of data
Parameter estimation/threshold setting
Detection
Preliminary processing
NASA-DAAC L0, L1 calibrations and geocoding
NASA-DAAC Land-See mask MOD 03
NASA-DAAC Cloud Mask MOD 35 (Modified)
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
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)
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
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
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[K]
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