Mapping forest and peat fires using hyperspectral airborne remote-sensing data

The characteristic features of airspace hyperspectral remote sensing (RS) are considered in order to develop classification techniques for relevant images. Currently available approaches to constructing classifiers (computational procedures) are described for recognizing natural and anthropogenic objects in hyperspectral images. We confirm that the methods under development are effective enough with the reduced dimensionality of the feature space of original spectra and the decreased sample volumes in supervising procedures for the selected object classes. Data from joint hyperspectral and aerial photography provide examples of the spectral distributions smoke of different intensities from forest and peat fires in the presence and absence of fire sources, for the smoke coverage of water surfaces, and for the forest vegetation without ignition sources within a selected area. The results obtained in the supervising procedures are used for pattern recognition and scene analysis in airborne images obtained for the test areas during forest-fire season.