Show simple item record

dc.contributor.authorGarcia, Charles
dc.date.accessioned2015-10-14T21:32:12Z
dc.date.available2015-10-14T21:32:12Z
dc.date.issued2015-06
dc.identifier.urihttp://hdl.handle.net/1773/34220
dc.descriptionSenior thesis written for Oceanography 445en_US
dc.description.abstract[author abstract] Sonar is the primary tool used to remotely survey the seafloor. Sub-bottom profilers use pulses of sound to penetrate the seafloor and create an acoustic profile of the seafloor and sub- bottom. The profiles are traditionally inspected by sight to identify features of interest which is time-consuming and requires an experienced human’s eye. I have developed a method using eigenimage analysis that can automatically distinguish between three different types of seafloor features found in the fjords of Nootka Sound: Sediment, sills, and rockslides. The method uses a training set of features to build eigenimages that represent the orthogonal variance between different features. Test features are projected onto the eigenimages and compared to the average three feature types. The resulting projection coefficients are a signature characteristic to each feature type and can be used to identify and classify seafloor features.en_US
dc.description.sponsorshipUniversity of Washington School of Oceanographyen_US
dc.language.isoen_USen_US
dc.subjectSubmarine geologyen_US
dc.subjectSonaren_US
dc.subjectNootka Sounden_US
dc.titleAutomatic recognition of seafloor features in sub-bottom profiles using eigenimagesen_US
dc.typeOtheren_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record