Feasibility of using template-based and object-based automated detection methods for quantifying black and hybrid imported fire ant (Solenopsis invicta and S. invicta × richteri) mounds in aerial digital imagery

Imported fire ants construct earthen nests (mounds) that exhibit many characteristics which make them potentially good targets for remote sensing programs, including geographical orientation, topography, and bare soil surrounded by actively growing vegetation. Template-based features and object-based features extracted from aerial multispectral imagery of fire ant infested pastures were used to construct classifiers for automated fire ant mound detection. Aclassifierconstructedusingtemplate-basedfeaturesaloneyieldeda79%probabilityofdetectionwithacorrespondingfalse positiverateof9%.Additionofobject-basedfeatures(compactnessandsymmetry)totheclassifieryieldeda79%probability ofdetectionwithacorrespondingfalsepositiverateof4%.Maintaininga79%detectionratewhenapplyingtheclassifiertoa second, unique pasture dataset with different seasonal and other environmental factors resulted in a false positive rate of 17.5%. Data demonstrate that automated detection of mounds with classifiers incorporating template- and object-based features is feasible, but it may be necessary to construct unique classifiers on a site-specific basis.