Automatic Recognition of Black-Necked Swan (Cygnus melancoryphus) from Drone Imagery

Abstract

Ecological 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 km2 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.