Graph-based Multibeam Forward Looking Acoustic Image Classification - Evangelista, Gabriel Arruda; Oliveira e Souza Filho, João Baptista de (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2023: Anais do XX Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2023.234431, 2023)
Multibeam sonar imaging has many applications, such as mine-like detection and navigation tasks, motivating interest in the automatic classification of sonar images. Recent works have proposed graph neural networks (GNNs) as an alternative to convolutional neural networks (CNNs) to address this task. This paper focuses on combining the strengths of both models to enhance the performance of GNNs when classifying sonar images. This proposal exploits a superpixel algorithm for image segmentation and graph formation. Comprehensive experiments with an MFLS open dataset evaluate the effect of model design parameters on the performance of the proposed approach. Using CNN-extracted features as initial node embeddings significantly improved the graph-based image classification performance.