Visible and thermal images acquired from drones (unoccupied aircraft systems) have substantially improved animal monitoring. Combining complementary information from both image types provides a powerful approach for automating detection and classification of multiple animal species to augment drone surveys. We compared eight image fusion methods using thermal and visible drone images combined with two supervised deep learning models, to evaluate the detection and classification of white-tailed deer (Odocoileus virginianus), domestic cow (Bos taurus), and domestic horse (Equus caballus). We classified visible and thermal images separately and compared them with the results of image fusion. Fused images provided minimal improvement for cows and horses compared to visible images alone, likely because the size, shape, and color of these species made them conspicuous against the background. For white-tailed deer, which were typically cryptic against their backgrounds and often in shadows in visible images, the added information from thermal images improved detection and classification in fusion methods from 15 to 85%. Our results suggest that image fusion is ideal for surveying animals inconspicuous from their backgrounds, and our approach uses few image pairs to train compared to typical machine-learning methods. We discuss computational and field considerations to improve drone surveys using our fusion approach.
Abstract-A pair of target locations are separable if sensor observations can distinguish between the following choices: no targets are present, one target is present at either of the locations or a target is present at each location. The sensors of interest in this paper are binary proximity sensors, whose binary outputs are functions of the distance between the sensor and target. Sensors are deployed randomly according to a Poisson distribution. The probability that two target locations at a distance of r between them are separable is derived. This is extended to derive the probability of having at least Z among M uniformly distributed target locations to be non-separable from the origin. The bounds on this probability are expressed as a function of the sensor density λ.
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