Performance estimation of parameter fusion strategies for improved seafloor classification for Mn-crust survey using AUV data

Neettiyath, Umesh, Sugimatsu, Harumi and Thornton, Blair (2024) Performance estimation of parameter fusion strategies for improved seafloor classification for Mn-crust survey using AUV data. In OCEANS 2024 - Singapore. IEEE. 4 pp . (doi:10.1109/OCEANS51537.2024.10682347).

Record type: Conference or Workshop Item (Paper)

Abstract

This paper compares the performance of seafloor classification methods based on data collected by autonomous robotic surveys of Cobalt-rich Manganese Crust deposits. Parameters were extracted from acoustic subbottom data using an autoencoder automatically, whereas visual data in the form of 3D color point clouds were analyzed to calculate physical parameters using mathematical equations. SVM classifiers were trained for each sensor modality, which showed that the acoustic classifier performed better than visual classifier. Middle fusion method, in which both parameter vectors are combined into a unified feature vector, performed better on almost every measure of performance than the previous two. Furthermore, it could prevent misclassification from both modalities.

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