An autologistic regression model for increasing the accuracy of burned surface mapping using Landsat Thematic Mapper data

An autologistic regression model, which takes into account neighbouring associations, was developed and applied for burned land mapping using Landsat-5 Thematic Mapper data. The integration of the autocovariate component (estimated using a moving window of 3 x 3 pixels) into the ordinary logistic regression model increased significantly the overall accuracy from 88.18% to 92.44%. In contrast, the accuracy derived with application of post-classification majority filters, which follow the same principles, were not significantly different to that derived with ordinary logistic regression.