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.

For marine accidents, prompt actions to minimize the casualties and loss of property are crucial. Remote sensing using satellites or aircrafts enables effective monitoring over a large area. Hyperspectral remote sensing allows the acquisition of high-resolution spectral information. This technology detects target objects by analyzing the spectrum for each pixel. We present a clustering method of seawater and floating objects by analyzing aerial hyperspectral images. For clustering, unsupervised learning algorithms of K-means, Gaussian Mixture, and DBSCAN are used. The detection performance of those algorithms is expressed as the precision, recall, and F1 Score. In addition, this study presents a color mapping method that analyzes the detected small object using cosine similarity. This technology can minimize future casualties and property loss by enabling rapid aircraft and maritime search, ocean monitoring, and preparations against marine accidents.

본 연구는 제한된 공정 데이터 환경에서 도장의 온도를 예측하기 위한 머신러닝 및 딥러닝 모델의 성능을 비교 분석하였다. 작업동 센서 데이터와 ASOS 기상 데이터를 결합하여 Ridge, Lasso, Random Forest와 같은 머신러닝 기법 모델과 RNN, LSTM과 같은 딥러닝 기법 모델의 평가를 진행하였다. 평가 시에는 보수적인 조선소 운영 환경을 고려하여 단순 정확도 뿐만 아니라 예측 변동성과 통계적 유의성을 함께 검토하였다. 연구결과, 순환 신경망 모델들이 높은 안정성과 우수한 예측 성능을 보여주었다. 이러한 결과는 도장 품질 향상과 에너지 효율화를 위한 스마트 조선소 운영 전략 수립에 기여할 것으로 기대되며, 향후 이를 기반으로 한 고도화 연구를 수행하고자 한다.