Constant False Alarm Rate In Fire Detection For Modis Data

Constant False Alarm Rate in Fire Detection for MODIS Data

This paper introduces the concept of Constant False Alarm Rate (CFAR) in fire detection for multispectral satellite data. A new algorithm is proposed, based on a technique successfully applied for detection of extended objects in High Resolution SAR images. It compares the pixel under analysis with an adaptive threshold, suitably estimated from the pixels surrounding the one under test, in order to ensure the CFAR property. The proposed approach requires that the background distribution is of Location Scale (LS) type or amenable to such a distribution by a suitable transformation. MODIS data from the 4-micrometer channel are considered. A preliminary statistical analysis is performed to verify if the Weibull distribution, compliant with LS representation, can be adopted for background. MODIS cloud and water masking are applied to identify those pixels to be discarded before implementing the statistical analysis. Results of fire detection are presented for different values of the system parameters (censoring depth and false alarm rate) and compared with the algorithm implemented in the NASA-DAAC MOD14.

Targets and Backgrounds

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones.