Convolutional Neural Networks for Satellite-Derived Bathymetry

Abstract

Bathymetric depth for shallow water regions is essential for coastal management and research. The measurements using echo sounding and LiDAR leave data gaps because vessels cannot reach nearshore waters or the green laser unable to penetrate specific areas. Satellite-Derived Bathymetry (SDB) is an alternative to extract shallow water depths that is able to overcome these problems using multispectral imagery. There are two approaches of SDB: analytical and empirical. The analytical method requires several water properties, which might not be known. The empirical method relies on the linear relationship between reflectances and depths, but the relationship may not be entirely linear due to bottom types variation, water column, and noise. Machine learning approaches have been used to address nonlinearity, but it treats pixels independently, whereas there is a spatial correlation that influences SDB computations since adjacent pixels are correlated to depth. This characteristic of the local connectivity can be captured by Convolution Neural Networks (CNN). Therefore, this thesis conducts a study of SDB using CNN.

This research focuses on the following questions: (i) what kind of preprocessing is needed for the data sets; (ii) what kind of CNN architecture can be used; (iii) what is the accuracy of the method; and (iv) to what extent the pretrained model in certain areas can be reused in other areas. In order to represent a variety of depth, bottom type, turbidity, and water column properties, this study chooses six areas of interest in three different coastal regions: Puerto Rico, Key West, and Hawaii.

With several CNN configurations, the optimum accuracy is obtained using three convolutional layers, a window size of 9x9, and the RGBNSS bands. Based on the experiment and comparison to the previous studies, the accuracy of SDB using the CNN approach outperforms the linear transform, the ratio transform, Random Forest, and the radiative transfer model. The results show that the accuracy decreases as the depth increases and in more turbid water. Comparison between different image preprocessing indicates another benefit of CNN: removing the need to preprocess images since suitable corrections can be automatically performed by CNN given adequate training.

The use of multi-temporal images enhances the variety of training data and thus improves SDB accuracy. However, data variation should be equally distributed to avoid abnormality in the result. Transfer model analysis indicates several limitations of SDB results at particular depths or when implemented to a different water condition, making the coastal water characteristics considered when reusing a pretrained model from one area to another.

In summary, CNN does not require additional image preprocessing and features specifications for training. CNN can produce better SDB accuracy than several other methods. The accuracy improves by increasing the variety of training data. However, SDB using the transfer model still need to be further investigated. A thorough identification of the proportion of sample data is needed to obtain balanced training data. In this way, it is more likely to produce a more reliable and more stable CNN model for extracting shallow water depths in the new data.