Machine learning-based region of interest detection in airborne lidar fisheries surveys

Trevor C. Vannoy; Jackson Belford; Joseph N. Aist; Kyle R. Rust; Michael R. Roddewig; James H. Churnside; Joseph A. A. Shaw; Bradley M. Whitaker

doi:10.1117/1.JRS.15.038503

19 July 2021 Machine learning-based region of interest detection in airborne lidar fisheries surveys

Trevor C. Vannoy, Jackson Belford, Joseph N. Aist, Kyle R. Rust, Michael R. Roddewig, James H. Churnside, Joseph A. A. Shaw, Bradley M. Whitaker

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 15, Issue 3, 038503 (July 2021). https://doi.org/10.1117/1.JRS.15.038503

Abstract

Airborne lidar data for fishery surveys often do not contain physics-based features that can be used to identify fish; consequently, the fish must be manually identified, which is a time-consuming process. To reduce the time required to identify fish, supervised machine learning was successfully applied to lidar data from fishery surveys to automate the process of identifying regions with a high probability of containing fish. Using data from Yellowstone Lake and the Gulf of Mexico, multiple experiments were run to simulate real-world scenarios. Although the human cannot be fully removed from the loop, the amount of data that would require manual inspection was reduced by 61.14% and 26.8% in the Yellowstone Lake and Gulf of Mexico datasets, respectively.

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Trevor C. Vannoy, Jackson Belford, Joseph N. Aist, Kyle R. Rust, Michael R. Roddewig, James H. Churnside, Joseph A. A. Shaw, and Bradley M. Whitaker "Machine learning-based region of interest detection in airborne lidar fisheries surveys," Journal of Applied Remote Sensing 15(3), 038503 (19 July 2021). https://doi.org/10.1117/1.JRS.15.038503

Received: 13 January 2021; Accepted: 2 July 2021; Published: 19 July 2021

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