Classification of lakebed geologic substrate in autonomously collected benthic imagery using machine learning
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- More information: Publisher Index Page (via DOI)
- Data Release: USGS data release - Autonomously Collected Benthic Imagery for Substrate Prediction, Lake Michigan 2020-2021
- Open Access Version: Publisher Index Page
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Abstract
Mapping benthic habitats with bathymetric, acoustic, and spectral data requires georeferenced ground-truth information about habitat types and characteristics. New technologies like autonomous underwater vehicles (AUVs) collect tens of thousands of images per mission making image-based ground truthing particularly attractive. Two types of machine learning (ML) models, random forest (RF) and deep neural network (DNN), were tested to determine whether ML models could serve as an accurate substitute for manual classification of AUV images for substrate type interpretation. RF models were trained to predict substrate class as a function of texture, edge, and intensity metrics (i.e., features) calculated for each image. Models were tested using a manually classified image dataset with 9-, 6-, and 2-class schemes based on the Coastal and Marine Ecological Classification Standard (CMECS). Results suggest that both RF and DNN models achieve comparable accuracies, with the 9-class models being least accurate (~73–78%) and the 2-class models being the most accurate (~95–96%). However, the DNN models were more efficient to train and apply because they did not require feature estimation before training or classification. Integrating ML models into benthic habitat mapping process can improve our ability to efficiently and accurately ground-truth large areas of benthic habitat using AUV or similar images.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Classification of lakebed geologic substrate in autonomously collected benthic imagery using machine learning |
| Series title | Remote Sensing |
| DOI | 10.3390/rs16071264 |
| Volume | 16 |
| Issue | 7 |
| Year Published | 2024 |
| Language | English |
| Publisher | MDPI |
| Contributing office(s) | Great Lakes Science Center |
| Description | 1264, 29 p. |
| Country | United States |
| State | Michigan, Wisconsin |
| Google Analytic Metrics | Metrics page |