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Article

Comparing Interpretation of High-Resolution Aerial Imagery by Humans and Artificial Intelligence to Detect an Invasive Tree Species

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Department of Molecular Biosciences and Bioengineering, University of Hawai’i at Manoa, Honolulu, HI 96822, USA
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Department of Geography and Environmental Science, University of Hawai’i at Hilo, Hilo, HI 96720, USA
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Center for Aquatic and Invasive Plants Aquatic and Invasive Plants, Department of Agronomy, University of Florida, Gainesville, FL 32653, USA
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Security in Silicon Laboratory, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32653, USA
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Department of Computer Science, University of Hawai’i at Hilo, Hilo, HI 96720, USA
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Spatial Data Analysis and Visualization Lab, University of Hawai’i at Hilo, Hilo, HI 96720, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Saeid Homayouni
Remote Sens. 2021, 13(17), 3503; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173503
Received: 20 July 2021 / Revised: 19 August 2021 / Accepted: 31 August 2021 / Published: 3 September 2021
(This article belongs to the Section AI Remote Sensing)
Timely, accurate maps of invasive plant species are critical for making appropriate management decisions to eliminate emerging target populations or contain infestations. High-resolution aerial imagery is routinely used to map, monitor, and detect invasive plant populations. While conventional image interpretation involving human analysts is straightforward, it can require high demands for time and resources to produce useful intelligence. We compared the performance of human analysts with a custom Retinanet-based deep convolutional neural network (DNN) for detecting individual miconia (Miconia calvescens DC) plants, using high-resolution unmanned aerial system (UAS) imagery collected over lowland tropical forests in Hawai’i. Human analysts (n = 38) examined imagery at three linear scrolling speeds (100, 200 and 300 px/s), achieving miconia detection recalls of 74 ± 3%, 60 ± 3%, and 50 ± 3%, respectively. The DNN achieved 83 ± 3% recall and completed the image analysis in 1% of the time of the fastest scrolling speed tested. Human analysts could discriminate large miconia leaf clusters better than isolated individual leaves, while the DNN detection efficacy was independent of leaf cluster size. Optically, the contrast in the red and green color channels and all three (i.e., red, green, and blue) signal to clutter ratios (SCR) were significant factors for human detection, while only the red channel contrast, and the red and green SCRs were significant factors for the DNN. A linear cost analysis estimated the operational use of a DNN to be more cost effective than human photo interpretation when the cumulative search area exceeds a minimum area. For invasive species like miconia, which can stochastically spread propagules across thousands of ha, the DNN provides a more efficient option for detecting incipient, immature miconia across large expanses of forested canopy. Increasing operational capacity for large-scale surveillance with a DNN-based image analysis workflow can provide more rapid comprehension of invasive plant abundance and distribution in forested watersheds and may become strategically vital to containing these invasions. View Full-Text
Keywords: deep neural network; unmanned aircraft system; aerial imagery; invasive species; performance evaluation; machine learning deep neural network; unmanned aircraft system; aerial imagery; invasive species; performance evaluation; machine learning
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MDPI and ACS Style

Rodriguez, R., III; Perroy, R.L.; Leary, J.; Jenkins, D.; Panoff, M.; Mandel, T.; Perez, P. Comparing Interpretation of High-Resolution Aerial Imagery by Humans and Artificial Intelligence to Detect an Invasive Tree Species. Remote Sens. 2021, 13, 3503. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173503

AMA Style

Rodriguez R III, Perroy RL, Leary J, Jenkins D, Panoff M, Mandel T, Perez P. Comparing Interpretation of High-Resolution Aerial Imagery by Humans and Artificial Intelligence to Detect an Invasive Tree Species. Remote Sensing. 2021; 13(17):3503. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173503

Chicago/Turabian Style

Rodriguez, Roberto, III, Ryan L. Perroy, James Leary, Daniel Jenkins, Max Panoff, Travis Mandel, and Patricia Perez. 2021. "Comparing Interpretation of High-Resolution Aerial Imagery by Humans and Artificial Intelligence to Detect an Invasive Tree Species" Remote Sensing 13, no. 17: 3503. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173503

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