Bathymetry Retrieval from Hyperspectral Image Using the Channel-wise Spectral Attention Based Convolutional Neural Network

Different from traditional measuring method based on the shipborne special equipment, satellite-based method has various potential advantages. This paper investigates the bathymetry retrieval problem from the hyperspectral remote sensing image. The idea is to make use of each spectral data, and fifind the relevant ones for water depth through the spectral attention weights. To fully exploit the spatial-spectral data, a convolutional neural network (CNN) is employed. It is fed with the small square hyperspectral patches and is required to output the bathymetry for the center point in the patch. The CNN has a side branch which outputs the attention weight for each channel, and it emphasizes the important ones by specifying a large value for it. Together with the model parameters, the attention weight helps mining the hyperspectral data for the accurate prediction.