• Subodh Prasad
• Pooja Tamta

Maritime surveillance is critical for ensuring safe navigation, securing international trade, and protecting marine ecosystems. While traditional systems such as radar and the Automated Identification System (AIS) offer valuable data, they often miss anomalies such as unidentified or suspicious vessel behavior. Recent advances in deep learning have opened new avenues for detecting anomalies by automatically analyzing high-resolution optical satellite imagery and multi-sensor data. This paper presents a comprehensive study of deep learning approaches for anomaly detection in maritime surveillance. We propose a novel framework that integrates convolutional neural networks (CNNs) with recurrent neural networks (RNNs) to capture both spatial and temporal features. Detailed discussions are provided on data collection, preprocessing, network architecture, training, and inference. In addition, the paper offers insights into challenges such as environmental variability and data imbalance, and presents case studies, sample program code, and diagrams that illustrate the methodology and results. Our experiments demonstrate that the proposed deep learning framework can effectively detect anomalous vessel behaviors and suspicious activities, thereby enhancing maritime situational awareness.

Deep Learning, Maritime Surveillance, Convolutional Neural Network, CNN.

"Deep Learning Approaches for Anomaly Detection in Maritime Surveillance", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.5-16, December-2018, Available :http://www.jetir.org/papers/JETIR1812F61.pdf

"Deep Learning Approaches for Anomaly Detection in Maritime Surveillance", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp5-16, December-2018, Available at : http://www.jetir.org/papers/JETIR1812F61.pdf