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