Back Propagation Neural Networks for Bathymetry Modeling Using Multispectral Data,
Abstract
The Naval Oceanographic and Atmospheric Research Laboratory (NOARL) has investigated using back propagation neural networks to model bathymetry from multispectral data. By using three multispectral bands, bathymetry estimation is possible. Traditional computational models fail to handle wide ranges in bathymetry sue to the highly non-linear nature of the input data. Back propagation neural networks, which are highly non-linear in nature, were selected to determine if faster and more accurate results could be achieved over larger ranges of bathymetry values. To evaluate this approach, 211 known spectral values for the three bands and their registered bathymetry were used as control and experimental sets. The data were sorted by increasing values of depth. The training set was obtained by taking every other value from the initial data set and by running over several thousand training cycles. After the network has shown sufficient adjustment of the internal weights, the control set is presented to the network. The rms error for this set is 1.8 meters.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 05, 1991
- Accession Number
- ADA236731
Entities
People
- Henry Rosche. Iii
Organizations
- United States Naval Research Laboratory