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

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

doi:10.1117/1.jrs.15.038503

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…We also computed the maximum and mean values of the scalogram, the maximum change between adjacent values in the frequency axis, and the average skewness of the frequency bins. For a full list of features, see the feature ranking in Figure 9 and our code [59] Individual model hyperparameters and the cost of predicting false negatives were tuned using Bayesian optimization [60], using MATLAB's bayesopt function. Fifteen iterations were used during parameter tuning.…”

Section: Feature Engineering Methodsmentioning

confidence: 99%

“…The search range for the false negative cost was integers between 1 and 10. For brevity, view our source code [59] for more information on the model-specific hyperparameter search ranges. The data sampling and model hyperparameter tuning were performed sequentially, rather than together.…”

Section: Feature Engineering Methodsmentioning

confidence: 99%

“…The filter size, number of filters, and false negative cost for each model were tuned using the same procedure described in the previous section. For more architecture and training details, see our source code [59].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

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Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data

Vannoy,

Sweeney,

Shaw

et al. 2023

Remote Sensing

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Concerns about decreases in insect population and biodiversity, in addition to the need for monitoring insects in agriculture and disease control, have led to an increased need for automated, non-invasive monitoring techniques. To this end, entomological lidar systems have been developed and successfully used for detecting and classifying insects. However, the data produced by these lidar systems create several problems from a data analysis standpoint: the data can contain millions of observations, very few observations contain insects, and the background environment is non-stationary. This study compares the insect-detection performance of various supervised machine learning and unsupervised changepoint detection algorithms and provides commentary on the relative strengths of each method. We found that the supervised methods generally perform better than the changepoint detection methods, at the cost of needing labeled data. The supervised learning method with the highest Matthew’s Correlation Coefficient score on the testing set correctly identified 99.5% of the insect-containing images and 83.7% of the non-insect images; similarly, the best changepoint detection method correctly identified 83.2% of the insect-containing images and 84.2% of the non-insect images. Our results show that both types of methods can reduce the need for manual data analysis.

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Section: Feature Engineering Methodsmentioning

confidence: 99%

Section: Feature Engineering Methodsmentioning

confidence: 99%

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Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data

Vannoy,

Sweeney,

Shaw

et al. 2023

Remote Sensing

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“…While the authors of previous studies have used spatial filtering and thresholding to detect phytoplankton, the authors of one study encountered challenges in fine-tuning the filtering parameters and threshold levels when using this approach [32]. Churnside explored the potential of supervised machine learning for locating regions containing fish [33], and found that there is no universally applicable optimal model; instead, the selection of a classifier depends on the specific goals of the research.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Subsurface Phytoplankton Layer Detection in LiDAR Data through Supervised Machine Learning Techniques

Zhong,

Chen,

Zhang

2024

Remote Sensing

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Phytoplankton are the foundation of marine ecosystems and play a crucial role in determining the optical properties of seawater, which are critical for remote sensing applications. However, passive remote sensing techniques are limited to obtaining data from the near surface, and cannot provide information on the vertical distribution of the subsurface phytoplankton. In contrast, active LiDAR technology can provide detailed profiles of the subsurface phytoplankton layer (SPL). Nevertheless, the large amount of data generated by LiDAR brought a challenge, as traditional methods for SPL detection often require manual inspection. In this study, we investigated the application of supervised machine learning algorithms for the automatic recognition of SPL, with the aim of reducing the workload of manual detection. We evaluated five machine learning models—support vector machine (SVM), linear discriminant analysis (LDA), a neural network, decision trees, and RUSBoost—and measured their performance using metrics such as precision, recall, and F3 score. The study results suggest that RUSBoost outperforms the other algorithms, consistently achieving the highest F3 score in most of the test cases, with the neural network coming in second. To improve accuracy, RUSBoost is preferred, while the neural network is more advantageous due to its faster processing time. Additionally, we explored the spatial patterns and diurnal fluctuations of SPL captured by LiDAR. This study revealed a more pronounced presence of SPL at night during this experiment, thereby demonstrating the efficacy of LiDAR technology in the monitoring of the daily dynamics of subsurface phytoplankton layers.

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Review of airborne oceanic lidar remote sensing

Chen,

Zhang

et al. 2023

Intell. Mar. Technol. Syst.

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Airborne oceanic lidars act as an active remote sensing technique have been proved to be one of the most effective and reliable means of oceanic profile remote sensing. This review aims to provide a comprehensive overview of the principles, methodologies, applications, and prospects of oceanic lidar remote sensing. A survey of the previous studies and works related to these techniques is presented in this paper, emphasizing the different mechanism in system design as well as data processing algorithms and their applications in the remote sensing of oceanic environmental parameters. The airborne lidar systems with multi-channels are designed to significantly improve the data quality and resolution of oceanic biological and geographic profiles. Algorithms for biological product retrieval and simulation based on typical radiation transfer models are described here to stimulate future research into ocean biogeochemistry. The advancement of airborne lidar applications in the near future is also presented.

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

Cited by 3 publications

References 3 publications

Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data

Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data

Enhancing Subsurface Phytoplankton Layer Detection in LiDAR Data through Supervised Machine Learning Techniques

Review of airborne oceanic lidar remote sensing

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