Constant False Alarm Rate in Fire Detection for MODIS Data

1. Constant False Alarm Rate in Fire Detection for MODIS Data • Maurizio di Bisceglie • Roberto Episcopo • Lilli Galdi • Silvia Ullo • Università del Sannio - Benevento - Italy dbmeeting - Benevento 3 - 6 october 2005

2. Motivation and purpose • 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.

3. Outline • Design of the CFAR detector • Validation of the statistical model • Algorithm description • Experimental results

4. background-only hypothesis cell under test adaptive threshold Suppose X is a Gaussian rv • The concept of CFAR by an example

5. Real scenario • 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

6. location parameter scale parameter standard variate with and is constant if are proper estimators from the and ordered sample depends on and

7. Ranking preserves the LS property

8. Scheme of the CFAR detector System outline of the CFAR algorithm

9. Statistical analysis Hypothesis model for 4μm MODIS brightness temperature: 3-parameter Weibull • 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

10. Parameter estimation algorithm

11. Test area Terra/MODIS true color, July 19th 2004, Campania region, Southern Italy

12. Cramer-Von Mises distance Distance between theoretical and empirical CDFs of 4μm MODIS brightness temperature

13. Cumulative Distributions

14. Sketch of fire detection algorithm • Preliminary processing • Window selection/sizing • Logarithm/ranking/censoring of data • Parameter estimation/threshold setting • Detection

15. Preliminary processing • NASA-DAAC L0, L1 calibrations and geocoding • NASA-DAAC Land-See mask MOD 03 • NASA-DAAC Cloud Mask MOD 35 (Modified)

16. 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

17. 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)

18. and 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

19. 350 340 330 320 310 300 [K] 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

20. 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