A straightforward way to map burned areas from Remotely Sensed imagery is to integrate partial evidence of burn provided by multiple Spectral Indices (SIs). Our approach relies on fuzzy set theory to generate integrated layers of overall positive and negative evidence scores. In order to reduce commission errors, we propose the use of negative evidence for revising the overall positive evidence. Revised layers are input for a region growing algorithm to produce a map of burned areas. TM images, acquired over the Mediterranean area, were used to derive the SIs and to define the soft constraints (membership functions). The performance of the revision process is tested for a TM image acquired over Portugal: the revision decreases the commission error from 59.5% to 1.3% and increases the overall accuracy from 42.6% up to 91.3%.
Positive and negative information for assessing and revising scores of burn evidence
Stroppiana D;Bordogna G;Boschetti M;Carrara P;PA Brivio
2012
Abstract
A straightforward way to map burned areas from Remotely Sensed imagery is to integrate partial evidence of burn provided by multiple Spectral Indices (SIs). Our approach relies on fuzzy set theory to generate integrated layers of overall positive and negative evidence scores. In order to reduce commission errors, we propose the use of negative evidence for revising the overall positive evidence. Revised layers are input for a region growing algorithm to produce a map of burned areas. TM images, acquired over the Mediterranean area, were used to derive the SIs and to define the soft constraints (membership functions). The performance of the revision process is tested for a TM image acquired over Portugal: the revision decreases the commission error from 59.5% to 1.3% and increases the overall accuracy from 42.6% up to 91.3%.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
