Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier Models

Abstract

The identification of maritime objects is crucial for ensuring navigational safety, enabling effective environmental monitoring, and facilitating efficient maritime search and rescue operations. Given its ability to provide detailed spectral information, hyperspectral imaging has emerged as a powerful tool for analyzing the physical and chemical properties of target objects. This study proposes a novel maritime object identification framework that integrates hyperspectral imaging with machine learning models. Hyperspectral data from six ports in South Korea were collected using airborne sensors and subsequently processed into spectral statistics and RGB images. The processed data were then analyzed using classifier and convolutional neural network (CNN) models. The results obtained in this study show that CNN models achieved an average test accuracy of 90%, outperforming classifier models, which achieved 83%. Among the CNN models, EfficientNet B0 and Inception V3 demonstrated the best performance, with Inception V3 achieving a category-specific accuracy of 97% when weights were excluded. This study presents a robust and efficient framework for marine surveillance utilizing hyperspectral imaging and machine learning, offering significant potential for advancing marine detection and monitoring technologies.