This paper presents a fuzzy system approach using texture and color to classify living coral cover in underwater color
images acquired by an autonomous underwater vehicle (AUV). The proposed fuzzy system for classification consists in
the assigning of fuzzy memberships to different image features such as the mean, the spatial variance, the Gabor filter
response standard deviation, and the wavelet energy. These fuzzy sensors are applied to the different segments present
in the images. The segmentation of the images is previously done using the Homogeneity Coefficient Segmentation
Algorithm (LHC). The resulted classification of the regions is compared against ground truth maps of the images. A
correct classification over 80% was achieved in two different 25 images sets of two different areas.
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.