TITLE:
Comparison of Three Techniques to Identify and Count Individual Animals in Aerial Imagery
AUTHORS:
Pat A. Terletzky, Robert Douglas Ramsey
KEYWORDS:
Aerial Photography, ISODATA, Principal Components, Texture, Unsupervised Classification
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.7 No.3,
July
18,
2016
ABSTRACT: Whether a species is rare and requires
protection or is overabundant and needs control, an accurate estimate of
population size is essential for the development of conservation plans and management
goals. Current wildlife surveys are logistically difficult, frequently biased,
and time consuming. Therefore, there is a need to provide additional techniques
to improve survey methods for censusing wildlife species. We examined three
methods to enumerate animals in remotely sensed aerial imagery: manual photo
interpretation, an unsupervised classification, and multi- image, multi-step
technique. We compared the performance of the three techniques based on the
probability of correctly detecting animals, the probability of under-counting
animals (false positives), and the probability of over-counting animals (false
negatives). Manual photo-interpretation had a high probability of detecting an
animal (81% ± 24%), the lowest probability of over-counting an animal (8% ±
16%), and a relatively low probability of under-counting an animal (19% ± 24%).
An unsupervised, ISODATA classification with subtraction of a background image
had the highest probability of detecting an animal (82% ± 10%), a high
probability of over-counting an animal (69% ± 27%) but a low probability of
under-counting an animal (18% ± 18%). The multi-image, multi-step procedure
incorporated more information, but had the lowest probability of detecting an
animal (50% ± 26%), the highest probability of over-counting an animal (72% ±
26%), and the highest probability of under-counting an animal (50% ± 26%).
Manual interpreters better discriminated between animal and non-animal features
and had fewer over-counting errors (i.e., false positives) than either the
unsupervised classification or the multi-image, multi-step techniques
indicating that benefits of automation need to be weighed against potential
losses in accuracy. Identification and counting of animals in remotely sensed
imagery could provide wildlife managers with a tool to improve population
estimates and aid in enumerating animals across large natural systems.