Use of classification and segmentation of sidescan sonar images for long term registration

This article handles the possibility of using classification and segmentation of sidescan sonar images for long term registration. In our case, long term registration means to find the displacement between two images which can have been mapped with many weeks or many months between them. The aim of...

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Bibliographic Details
Main Authors: Leblond, I., Legris, M., Solaiman, B.
Format: Conference Proceeding
Language:English
Subjects:
Geometry
Image segmentation
Marine animals
Physics
Sea floor
Sediments
Shape
Sonar navigation
Testing
Underwater vehicles
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Summary:This article handles the possibility of using classification and segmentation of sidescan sonar images for long term registration. In our case, long term registration means to find the displacement between two images which can have been mapped with many weeks or many months between them. The aim of this study is to help AUVs (autonomous underwater vehicles) to navigate, in particular to correct the drift of navigation sensors. This type of positioning raises two sorts of problems, which come from image properties: spatial variability and temporal variability. The first one is caused above all by the sonar geometry and appears for example like a modification of the shape or the position of the shadow according to the point of view. This effect can also be seen in textures, for example on megaripples of sand, which can be more or less visible depending on the point of view of the sonar. The second one is more the consequence of the seafloor physics: between two images, mapped at different times, some elements may have changed. An obvious example is the presence of evanescent "objects" like fishes but this variability can also be seen on sediments, which borders can move due to local bottom dynamics. With the aim to solve these problems and to provide reliable landmarks for matching, we decided to classify and segment the images. The data have first been corrected from TVG (time varying gain) effects and despeckelised in order to process images which are more representative of the seafloor. The basis idea is to use a supervised method of classification. To do that, we consider some parameters which are coming from a decomposition by Gabor filters, in order to segment with linear discriminant analysis and use of the nearest neighbour method. Registration needs accurately localised landmarks: so, this operation is split in several stages, refining step by step the classification, in order to obtain a map which describes the seafloor with the most possible detailed frontiers. Then, we present the obtained results considering five texture classes: rocks, megaripples, sand, mud and shadow. These several areas and their frontiers are the basis landmarks to match the images. However, before using the segmentation, we must check its reliability. So, it appears that the frontiers, though they are realistic, are not accurate enough to make a registration precise to few pixels, especially in rock areas. Similarly, according to the orientation of the ripples, they may be
DOI:10.1109/OCEANSE.2005.1511734