Classification of Seabed Sediment by Combining Airborne LiDAR Bathymetry and Multispectral Remote Sensing Images
The classification of seabed sediment plays an important role in marine ecological environment protection and other related fields. To fully explore the application ability of marine geographic information in seabed sediment classification, this article makes a contribution to overcome the low accur...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10919041/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The classification of seabed sediment plays an important role in marine ecological environment protection and other related fields. To fully explore the application ability of marine geographic information in seabed sediment classification, this article makes a contribution to overcome the low accuracy and reliability shortcomings of using single data source and traditional classifiers. Based on extracted multisource features, the scale-invariant feature transform - random sample consensus model is applied to realize feature-level fusion between airborne LiDAR bathymetry (ALB) data and multispectral remote sensing images. Furthermore, a dual-branch convolutional neural network (CNN) classifier is constructed to classify the seabed sediment into five categories (coral reef, sand, gravel, coastal zone, and vegetation). To verify the effectiveness of fused data in seabed sediment classification, experiments were conducted using multispectral remote sensing images and ALB data. Experimental results show that the overall classification accuracy and the Kappa coefficient of the dual CNN classifier constructed in this article are 98.2% and 0.977, respectively. In addition, the classification results using multisource fusion data are higher than those using single-source data, indicating the accuracy and effectiveness of multisource fusion features for classification. The research results can provide effective technical support for seabed sediment classification. |
|---|---|
| ISSN: | 1939-1404 2151-1535 |