Bathymetry from Shallow Coastal Environment using Neural Network(Case Study: Southeastern of the Caspian Sea)

Remote sensing method known an appropriate tool for estimating depth in the coastal environment of the limited reaches. The purpose of this study is to measure the depth of the southeastern coast of the Caspian Sea through the training of the neural network. In order to estimate depth, Landsat-8 images and hydrographic data collected using the echosounder, have been used. Atmospheric correction of Dark Object Subtract (DOS), radiometric correction (turning digital number to reflection), the sun glint correction, and eventually masking the water body from the land area, applied on the coastal blue, blue, green and red bands. These steps known as pre-processing.
In this study, depth estimation through the neural network is investigated in two states. In the first case, each of the four bands as input and real depth corresponding to each of these pixels as target was introduced to the neural network. In the second case, the depth data were clustered to seven clusters by the fuzzy C-mean (FCM) method. After clustering, the data of each cluster was separately presented to the network. In both cases, the share of train data, validation data and test data from input data is 60%, 10% and 30%, respectively. The results of the neural network indicate that the accuracy of the estimated depth in various clusters is different, and the highest accuracy (R2 = 0.90, RMSE= 0.11) and the lowest accuracy (R2 = 0.67, RMSE= 0.11) belong to cluster (1) and cluster (3) respectively. As well as, the estimated depth in no clustered data, evaluated with high accuracy )R2 = 0.98, RMSE = 0.16).Then, neural network method is able to estimate depth from shallow coastal waters with high accuracy.