RT Journal Article
T1 Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems
A1 Longmore, SN
A1 Collins, RP
A1 Pfeifer, S
A1 Fox, SE
A1 Mulero-Pázmány, Margarita Cristina
A1 Bezombes, F
A1 Goodwin, A
A1 De Juan Ovelar, M
A1 Knappen, JH
A1 Wich, S
K1 Aviones sin piloto
AB In this article, we describe an unmanned aerial system equipped with a thermal-infrared camera and software pipeline that we have developed to monitor animal populations for conservation purposes. Taking a multi-disciplinary approach to tackle this problem, we use freely available astronomical source detection software and the associated expertise of astronomers, to efficiently and reliably detect humans and animals in aerial thermal-infrared footage. Combining this astronomical detection software with existing machine learning algorithms into a single, automated, end-to-end pipeline, we test the software using aerial video footage taken in a controlled, field-like environment. We demonstrate that the pipeline works reliably and describe how it can be used to estimate the completeness of different observational datasets to objects of a given type as a function of height, observing conditions, etc. – a crucial step in converting video footage to scientifically useful information such as the spatial distribution and density of different animal species. Finally, having demonstrated the potential utility of the system, we describe the steps we are taking to adapt the system for work in the field, in particular systematic monitoring of endangered species at National Parks around the world
YR 2017
FD 2017
LK https://hdl.handle.net/10630/33240
UL https://hdl.handle.net/10630/33240
LA eng
NO Longmore S.N., Collins R.P., Pfeifer S., Fox S.E., Mulero-Pazmany M., Bezombes F., Goodwin A., De Juan Ovelar M., Knappen, J.H., Wich, S. (2017). Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems. International Journal of Remote Sensing. 0: 1–16. https://doi.org/10.1080/01431161.2017.1280639
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 22 ene 2026