The ocean drives global processes that keep the Earth livable for humans; it is an important aspect of our biodiversity since it is home to a variety of marine creatures, regulates the temperature, and offers food. The ocean is also important for economic development, with a huge percentage of trade traveling through marine channels, supporting millions of people. More than 50,000 seagoing ships transport more than 10 billion tons of important and desirable cargo each year, including goods, fuels, natural resources, and consumer items [
1]. Though oceans have linked the world via trade and transportation, another aspect of the ocean is the various ship wreckages it brings with it. Various accidents have occurred in oceans and seas, either purposefully or unintentionally. The most common causes of marine casualties include capsizing, grounding, sinking, collision, or blockage. Oil and chemical spills, hazardous waste dumping, and bio invasion by foreign microbes are all causes of the environmental degradation of oceans.
Because of these concerns, marine security and safety are critical, and marine surveillance has risen in importance in recent years. Many techniques have been developed to make ship monitoring a simple operation, providing information on the presence and activities of ships, out of which cooperative and non-cooperative systems are two major categories. In the first system, vessels cooperate to disclose their identity and location. In the latter technology, vessel tracking systems (VMS) and automatic identification systems (AIS) are used to track maritime movement, along with providing information about the position and speed of the ship [
2]. To detect objects, these systems include sensors such as cameras and radar on a variety of platforms such as ships, airplanes, and satellites. Using aerial and satellite sensors, it is now possible to monitor marine vessels from a distance, independent of ground circumstances [
3]. Remote sensing data are useful in marine surveillance, and SAR imaging has made ship detection quite an easy process. SAR data are made up of the focused reflected returns of radar-frequency energy from terrain that has been brightened by a sensor’s directed pulse beam. The key parameters influencing radar returns from the topography are surface roughness, geometric structure, electrical characteristics (relative permittivity, conductivity), and sensor radar frequency. Sentinel-1 is an SAR satellite that acquires images with wide coverage and has the advantages of imaging irrespective of the weather conditions. Depending on the surface of the item, certain pixels look brighter while others appear darker. Because ships’ construction is irregular and rough, ships scatters more light and are also perpendicular to the route of propagation; therefore, more reflected light is received back by the radar, resulting in bright pixels. Smooth and calm objects, such as the water or ocean, reflect less light, so less light reaches the radar, resulting in darker patches. Because of this phenomenon alone, it is easier to see white patches against a dark backdrop, assisting in the detection of marine items distributed across a wide sea. In addition, Sentinel-2 datasets are high-resolution optical images that offer global coverage, hence making them useful for ship detection. Although the detection of ships is a major concern, it is also necessary to know other parameters of the detected ships, such as their size, length, vessel type, velocity, and so on. Various academics have used various approaches to estimate the length and direction of the vessel. Ship signature pixels are displayed as points on the Cartesian plane to determine the size and orientation of the ship. Point clouds have been obtained using the least squares line. The direction of the conforming line in relation to the range direction is used to compute the direction of the ship in relation to the range direction. Length is defined as the distance between the outermost pixels along the optimal line, and width is the distance orthogonal to the optimal line [
4]. Another parameter, namely vessel categorization, is still in an early stage compared to ship detection, which has progressed due to the development of numerous approaches. The major goal of vessel classification is identifying the best qualities that may be used for categorization. The process becomes rather challenging due to the complicated structure of vessels, since signatures fluctuate as a function of the observation angle [
5]. Various techniques are being proposed by various researchers for vessel classification utilizing the most recent deep learning technology. A novel convolutional network method has been created for this purpose, capable of classifying ships into several categories, such as cruise, tanker, cargo, military, and carrier. Aside from that, five pre-trained models have been employed, with Resnet-152 being named the best model and LexNet being named the poorest model with the lowest accuracy [
6]. Ma et al. (2018) discuss vessel classification at the patch level using a convolutional neural network [
7]. A novel architecture based on multitask neural networks was devised by Dechesne et al. (2019), consisting of a combined convolutional network linked to three task-specific networks for classification, ship identification, and length estimation [
8]. For the categorization of the structure, this model has an accuracy of 97.25 percent. A great deal of research is still being conducted in this field in order to make a breakthrough. Since the well-being of millions of people and industrial sectors is dependent on the ocean, it has become clear with each passing year that the ocean’s health is deteriorating due to numerous activities that entail the usage of the ocean. The operational discharge of hydrocarbons and their accumulation in water bodies from ships, both licit and illicit, is a primary factor in the development of surface slicks, which are a source of worry regarding both the sea surface and offshore [
9]. Illegal fishing is another major source of concern, as it frequently leads to resource overexploitation, along with other activities such as piracy, trafficking, smuggling, and so on. Ocean traffic is expanding rapidly as a result of the high demand for the commerce of diverse items across different countries. The congested waterways increase the risk of accidents, which has a negative impact on both the commercial and public sectors [
10]. This is quite evident from the major incident that occurred in the Suez Canal, which threw the worldwide supply chain into disarray. Due to heavy winds, the 400-meter-long cargo freighter named Ever Given became trapped across the body of water on the morning of 23 March 2021, with its bow and stern wedged in the Suez Canal bank. It persisted for six days, stopping all vessels from passing through and causing major congestion on both sides of the canal. The management of the Suez Canal during this incident was accomplished using Sentinel-1 images. Blocked ships were placed in different geographical places to allow ample distance between two vessels. Finally, Ever Given was freed on 29 March 2021, and vessels began to travel across the canal, alleviating gridlock. This one catastrophe served as a wake-up call for many authorities, demonstrating how the global economy may be badly impacted by a single incident. Many research groups went on to study this incident from various perspectives. From an economic view, the canal’s closure had a significant impact on trade between Europe, Asia, and the Middle East. According to the Suez Canal Authority, the increased traffic and delays in trade supplies cost them USD 14 million in revenue in a single day [
11]. A paper by Rashid et al. (2022) outlines marine traffic anomalies, such as the increased congestion caused by the Suez Canal barrier. An examination of satellite automated detection system-based ship trajectories, as well as of the Sentinel 1 and Sentinel 2 image-based ship positions, reveals that the blockage impacted maritime trade for more than three weeks [
12]. Another study employed AIS data and a spatiotemporal data cube to examine the change in the number of ships, the cargo capacity obstructed near the Suez Canal and the increased waiting times for ships outside nodal ports [
13]. Gast et al. (2021) developed a queueing model to study the recovery of an interrupted transport route, thereby predicting the delays induced by the disruption [
14]. Another study looked at the Suez Canal barrier from an environmental standpoint. Images from the Multitemporal Sentinel 2 proved valuable in determining the impact of the canal obstruction on the Total Suspended Matter (TSM) concentration [
15].
The primary goal of the study is to investigate the maritime traffic congestion caused by the Suez Canal blockage due to Ever Given’s stranding using the Sentinel-1 dataset. We examined the fluctuations in the obstructed vessels in the Gulf of Suez before, during, and after the incident and compared the findings to the Sentinel-2 dataset in order to determine the effectiveness of both datasets for ship identification. The suitability of polarimetric channels for ship detection is also determined.