Subsurface object recognition by means of regularization techniques for mapping coastal waters floor

Abstract

A fundamental challenge to Remote Sensing is mapping the ocean floor in coastal shallow waters where variability, due to the interaction between the coast and the sea, can bring significant disparity in the optical properties of the water column. The objects to be detected, coral reefs, sands and submerged aquatic vegetation, have weak signals, with temporal and spatial variation. In real scenarios the absorption and backscattering coefficients have spatial variation due to different sources of variability (river discharge, different depths of shallow waters, water currents) and temporal fluctuations. This paper presents the development of algorithms for retrieving information and its application to the recognition, classification and mapping of objects under coastal shallow waters. A mathematical model that simplifies the radiative transfer equation was used to quantify the interaction between the object of interest, the medium and the sensor. The retrieval of information requires the development of mathematical models and processing tools in the area of inversion, image reconstruction and detection. The algorithms developed were applied to one set of remotely sensed data: a high resolution HYPERION hyperspectral imagery. An inverse problem arises as this spectral data is used for mapping the ocean shallow waters floor. Tikhonov method of regularization was used in the inversion process to estimate the bottom albedo of the ocean floor using <i>a priori</i> information in the form of stored spectral signatures, previously measured, of objects of interest, such as sand, corals, and sea grass.