Automatic Recognition of Black-Necked Swan (<i>Cygnus melancoryphus</i>) from Drone Imagery

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dc.contributor.authorJimenez-Torres, Marina
dc.contributor.authorSilva, Carmen P. P.
dc.contributor.authorRiquelme, Carlos
dc.contributor.authorEstay, Sergio A. A.
dc.contributor.authorSoto-Gamboa, Mauricio
dc.date.accessioned2025-01-20T20:17:05Z
dc.date.available2025-01-20T20:17:05Z
dc.date.issued2023
dc.description.abstractEcological monitoring programs are fundamental to following natural-system populational trends. Drones are a new key to animal monitoring, presenting different benefits but two basic re-strictions First, the increase of information requires a high capacity of storage and, second, time invested in data analysis. We present a protocol to develop an automatic object recognizer to minimize analysis time and optimize data storage. We conducted this study at the Cruces River, Valdivia, Chile, using a Phantom 3 Advanced drone with an HD-standard camera. We used a Black-necked swan (Cygnus melancoryphus) as a model because it is abundant and has a contrasting color compared to the environment, making it easy detection. The drone flew 100 m from water surface (correcting AGL in relation to pilot landing altitude) obtaining georeferenced images with 75% overlap and developing approximately 0.69 km(2) of orthomosaics images. We estimated the swans' spectral signature to build the recognizer and adjusted nine criteria for object-oriented classification. We obtained 140 orthophotos classified into three brightness categories. We found that the Precision, Sensitivity, Specificity, and Accuracy indicator were higher than 0.93 and a calibration curve with R2= 0.991 for images without brightness. The recognizer prediction decreases with brightness but is corrected using ND8-16 filter lens. We discuss the importance of this recognizer to data analysis optimization and the advantage of using this recognition protocol for any object in ecological studies.
dc.fuente.origenWOS
dc.identifier.doi10.3390/drones7020071
dc.identifier.eissn2504-446X
dc.identifier.urihttps://doi.org/10.3390/drones7020071
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/92374
dc.identifier.wosidWOS:000939204100001
dc.issue.numero2
dc.language.isoen
dc.revistaDrones
dc.rightsacceso restringido
dc.subjectautomatic recognition
dc.subjectdrone
dc.subjectblack-necked swan
dc.subjectabundance and density estimation
dc.subjectorthomosaic object recognition
dc.subject.ods15 Life on Land
dc.subject.ods14 Life Below Water
dc.subject.odspa15 Vida de ecosistemas terrestres
dc.subject.odspa14 Vida submarina
dc.titleAutomatic Recognition of Black-Necked Swan (<i>Cygnus melancoryphus</i>) from Drone Imagery
dc.typeartículo
dc.volumen7
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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