WHDL - 00011886

Evaluation of Texture as a Fourth Input of Spatial Context Using Machine Learning to Map Wildland Fire Effects

Jonathan M. Branham

Description / Abstract

Submitted to the Department of Mathematics and Computer Science in partial fulfillment of the requirements for the degree of Bachelor of Arts

A variety of machine learning algorithms have been used to map wildland fire effects, but previous attempts to map post-fire effects have been conducted using relatively low-resolution satellite imagery. Small unmanned aircraft systems (sUAS) provide opportunities to acquire imagery with much higher spatial resolution than is possible with satellites or manned aircraft. This effort investigates improvements achievable in the accuracy of post-fire effects mapping with machine learning algorithms that use hyperspatial (sub-decimeter) UAS imagery. Spatial context using a variety of texture metrics were also evaluated to determine the inclusion of spatial context as an additional input to the analytic tools along with the three-color bands. This analysis shows that the addition of texture as an additional fourth input increases classifier accuracy when mapping post-fire effects.

Evaluation of Texture as a Fourth Input of Spatial Context Using Machine Learning to Map Wildland Fire Effects

Description / Abstract

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About the Authors

Jonathan M. Branham