A deep-learning model for the amplitude inversion of internal waves based on optical remote-sensing images

The amplitude of internal waves is an important parameter for the remote-sensing detection. However, there is no analytic method developed for the amplitude inversion of internal waves based on optical remote-sensing images. In this article, the deep-learning model is introduced to inverse the amplitude of internal waves based on a large number of optical remote-sensing images. The peak-to-peak distance and 15 types of texture characteristic parameters of images are computed, and the relationship between the amplitude of internal waves and the characteristic parameters of remote-sensing images are also investigated. In addition, based on the correlation difference between these parameters and the amplitude, three types of deep-learning model are established by selecting different parameters as input variables. Results show that the inverted amplitude of internal waves from our models shows good agreement with the in-situ data of internal waves, and the average errors between them are about 6.7%, 4.9%, and 3.6%, respectively, which indicates that the deep-learning model is effective for amplitude inversion of internal waves based on optical remote-sensing images.