Band-Optimized Bidirectional LSTM Deep Learning Model for Bathymetry Inversion

Abstract

Shallow water bathymetry is of great significance in understanding, managing, and protecting coastal ecological environments. Many studies have shown that both empirical models and deep learning models can achieve promising results from satellite imagery bathymetry inversion. However, the spectral information available today in multispectral or/and hyperspectral satellite images has not been explored thoroughly in many models. The Band-optimized Bidirectional Long Short-Term Memory (BoBiLSTM) model proposed in this paper feeds only the optimized bands and band ratios to the deep learning model, and a series of experiments were conducted in the shallow waters of Molokai Island, Hawaii, using hyperspectral satellite imagery (PRISMA) and multispectral satellite imagery (Sentinel-2) with ICESat-2 data and multibeam scan data as training data, respectively. The experimental results of the BoBiLSTM model demonstrate its robustness over other compared models. For example, using PRISMA data as the source image, the BoBiLSTM model achieves RMSE values of 0.82 m (using ICESat-2 as the training data) and 1.43 m (using multibeam as the training data), respectively, and because of using the bidirectional strategy, the inverted bathymetry reaches as far as a depth of 25 m. More importantly, the BoBiLSTM model does not overfit the data in general, which is one of its advantages over many other deep learning models. Unlike other deep learning models, which require a large amount of training data and all available bands as the inputs, the BoBiLSTM model can perform very well using equivalently less training data and a handful of bands and band ratios. With ICESat-2 data becoming commonly available and covering many shallow water regions around the world, the proposed BoBiLSTM model holds potential for bathymetry inversion for any region around the world where satellite images and ICESat-2 data are available.

Bathymetry Inversion Using Attention-Based Band Optimization Model for Hyperspectral or Multispectral Satellite Imagery

“…The distribution location of the ICESat-2 data is shown in Figure 2b, and the detailed information is shown in Table 2. The ICESat-2 photon data require data screening, refraction correction, and tidal correction to enhance the accuracy [24]. The specific process for the photon data in the Yinyu Island study area is as follows.…”

“…It can be seen that the optimal bands selected by the ABO-CNN model and the optimal band ratios selected by the Stumpf model basically agree with each other, which may indicate that these optimal bands belong to the effective bands for bathymetry inversion. This study utilizes the previous research results [24] on bathymetry inversion in Molokai Island to compare the bathymetry inversion accuracy and optimal bands between the BoBiLSTM model with stepwise band selection and the ABO-CNN model with simultaneous band optimization. Table 7 shows a comparison of errors between the retrieved bathymetry inversion maps from the PRISMA and Sentinel-2 images and the Reference Bathymetric Map.…”

“…Yang et al [23] improved the reward function of the double deep Q-network to select important bands, which were then inputted into multiple classifiers for land cover classification. Xi et al [24] employed the gradient boosting decision tree (GBDT) and the Stumpf model to select optimal bands and band ratios, which were used as inputs for a bathymetry inversion model. These methods typically involve band selection as a preprocessing step to reduce data processing complexity before performing specific tasks such as bathymetry inversion.…”