Bathymetry Retrieval From Hyperspectral Remote Sensing Data in Optical-Shallow Water

Abstract

In this paper, an algorithm for estimating shallow-water depth from hyperspectral data is proposed. This methodology is based on the different responses of shallow-water reflectance on depth and substrate type. Two parameters-similarity coefficient and Pearson correlation coefficient-are introduced to describe the different types of responses, and a linear logarithm ratio model is established. Using Hyperion data over the coastal regions of O'Ahu Island and Saint Thomas Island, the retrieved bathymetry is compared with the airborne LIDAR data. The validation results show that the proposed method has good performance, and the root mean square error is less than 1.5 m over shallow water (shallower than 20 m).