Unidentified flying objects can be aircraft that do not continuously broadcast ADS-B. They pose a risk for air traffic safety, territorial violation, espionage, etc. In this study, we introduce a method for detecting and estimating the state of aircraft in Sentinel-2 multispectral satellite images. We construct a dataset of 579 ADS-B annotated aircraft from 69 Sentinel-2 images. A CNN is trained on the dataset to estimate the aircraft state vector i.e. position, velocity, heading, altitude. This work allows real-time monitoring of flying objects in satellite images.

This paper presents an approach to increase the knowledge gained during maritime surveillance using high-resolution ICEYE Synthetic Aperture Radar (SAR) imagery, by estimating the three-dimensional features of vessels. Specifically, the height and mass, alongside traditional two-dimensional length, and width parameters. By analysing the SAR shadow and overlay, we calculate the height of the deck and bridge on the vessel, leading to an estimation of its cargo weight. This enhances maritime surveillance by providing a deeper understanding of vessel characteristics critical for security applications. While we utilize Ultralytics’ YOLOv8 deep learning for initial ship detection, the primary focus is on the detailed estimation of 3D features, a capability not previously demonstrated with SAR imagery.

Ships may be found by different types of satellite sensors including Synthetic Aperture Radars (SAR), multispectral, Automatic Identification Systems (AIS) and RF electronic support systems. Several methods are described for detecting, classifying, and identify (ID) ships as well as discriminating them from, e.g., icebergs. For example, AIS data can be used in combination with other sensors both for annotation and for finding dark ships, i.e., ships that have turned off their transponder. The sensor fusion, data combination and matching of various sensor data spatially and temporally are then important. We analyze methods and results for various combinations of sensor data, where we use deep neural networks on annotated datasets for detection, classification, discrimination and ID of ships.

Non-cooperative vessels pose a challenge to traditional maritime surveillance systems. To overcome this challenge, alternative surveillance methods such as space-based monitoring sensors have been employed. However, the time-consuming process of satellite downlink hampers near-real-time applications. To address these issues, the use of onboard Artificial Intelligence for direct data processing has emerged as a key technology. This study explores the implementation of a lightweight Synthetic Aperture Radar ship detection model inspired by YOLOv8. The model achieves promising results on an annotated data-set, demonstrating the effectiveness of the approach for detecting both small and large ships. The study investigates the impact of atrous and depth-wise convolutions on the model’s performance and explores model quantization for further size reduction. Our final model has 0.3 million parameters and reached an average procession of 95.4 %. The results highlight the potential of lightweight models for onboard ship detection, offering comparable accuracy to larger models.

This article presents a methodology for localizing radio frequency interference (RFI) signals in Synthetic Aperture Radar (SAR) images acquired from Sentinel-1 SAR satellites. RFI are caused by on-ground radars, and their detection and localization thus provide valuable information for decision makers. In this study, an unsupervised deep learning model based on a Convolutional Autoencoder is used to detect and localize RFI signals in SAR images. The CAE reconstructs the SAR images, excluding RFI signals and other large-scale anomalies. Anomalies are detected by comparing the original images with their reconstructions, and a secondary classification scheme is used to identify RFI signals among the detected anomalies. Results show that the proposed method detects and localizes RFI signals, even in complex regions. The automatic localization of RFI signals in SAR images can enhance various applications such as maritime domain awareness and border surveillance.

Synthetic Aperture Radar (SAR) satellite images are used increasingly more for Earth observation. While SAR images are useable in most conditions, they occasionally experience image degradation due to interfering signals from external radars, called Radio Frequency Interference (RFI). RFI affected images are often discarded in further analysis or pre-processed to remove the RFI. However, few on-ground radars can cause RFI in SAR images and such information can thus increase domain awareness greatly over both land and sea, where,e.g., localizing and characterizing RFI signals in the ocean could help classify otherwise overlooked ships. The aim of the current study is to detect and localize RFI signals automatically in Sentinel-1 level-1 images and further characterize the on-ground radar. The spatial structure of RFI signals vary greatly. A convolutional autoencoder was therefore developed to reconstruct RFI-free Sentinel-1 images. Conversely, RFI-affected images could not be well reconstructed. Anomalous heatmaps were then developed to automatically detect and localize RFI anomalies in the images under varying environmental and geographical conditions whereafter the external radar characteristics were extracted manually from Sentinel-1 level-0 data. We could consequently classify and localize RFI signals believed to originate from both stationary radars and ship-borne radars. We further argue that the calculated ship-borne radar characteristics correspond to those of air-surveillance radars. Empirically, the method showed better detection results than those of previous studies. Our study shows that more information can be extracted from certain detected objects, such as ships, from SAR images.