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3 results for “ship wakes”
MSSWD - Multi-Spectral Ship Wake Dataset
<p>The <strong>Multi-Spectral Ship Wake Dataset (MSSWD)</strong> is a dataset designed for ship wake detection in multi-spectral satellite imagery. It is structured as follows:</p> <p>- <strong>Source</strong>: 661 image chips derived from 50 Sentinel-2 images, captured by the Multi-Spectral Instrument (MSI) at 10-meter resolution across the visible, near-infrared (VNIR), and short-wave infrared (SWIR) spectral bands. The chips come already pre-processed to highlight sea surface features by using a Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. <br> <br>- <strong>Content</strong>: The dataset includes 1059 ship wakes, with various configurations such as:<br> - Single ship wakes<br> - Multiple ship wakes<br> - False wakes (e.g., airplane wakes, sea crests)<br> - Sea clutter with no visible wakes</p> <p>- <strong>Wake Characteristics</strong>: Diverse patterns of ship wakes are captured, including:<br> - Vertical, horizontal, and tilted wakes<br> - Cluttered sea scenes<br> - Partial occlusions due to cloud cover</p> <p>- <strong>Data Quality</strong>: Focused on <em>quality over quantity</em>, MSSWD reflects real-world complexity by collecting data in congested, crowded maritime environments.</p> <p>- <strong>Data Labelling</strong>: Manually annotated using polygonal annotations to delineate wake contours, which allows:<br> - Instance segmentation<br> - Enhanced refinement during data augmentation</p>
Keypoints Method for Recognition of Ship Wake Components in Sentinel-2 Images by Deep Learning
<p>The dataset used in the study consists of imagery capturing ship wake patterns. It is a manually curated dataset specifically created for the purpose of training and evaluating the wake component detection model. The dataset contains a collection of image chips, each focusing on a specific ship wake instance.</p> <p>The imagery in the dataset is acquired from satellite sensors, specifically on Sentinel-2 satellite imagery. Sentinel-2 provides multispectral data with high spatial resolution, allowing for detailed analysis of ship wake patterns. The dataset includes images captured on B8 spectral band, enabling the exploration of the wake detection model's performance under various spectral conditions. These images have been pre-processed (by scaling+CLAHE) to highlight ocean surface features.</p> <p>Each image chip in the dataset is annotated with keypoint locations representing specific wake components, such as the ship wake vertex, the ending of the turbulent wake, and the ending of Kelvin arms. These annotations serve as ground truth labels for training and evaluating the wake component detection model. </p> <p>Additionally, the dataset includes samples with variations in environmental conditions, such as different sea states, lighting conditions, and wake complexities. This variability allows for a comprehensive evaluation of the model's generalization capability and robustness across diverse scenarios.</p>
xAI Ship Wakes in Sentinel-2 L2A images
<h2><strong>xS2Wakes: A dataset for xAI of Wakes in S-2 (L2A).</strong></h2><h3><strong>Summary</strong></h3><p>The dataset is derived from Sentinel-2 Level-2A (L2A) satellite images and focuses on the marine domain over Danish fjords. It provides a comprehensive collection of ship wakes and background clutter (referred to as "no_<i>wake</i>_crop") for remote sensing applications. The dataset has undergone post-processing through the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm with a clip limit value of 0.12 and a tile size of 16x16. The dataset comprises four spectral bands: B2, B3, B4, and B8.</p><h3><strong>Importance and Relevance to Remote Sensing Community</strong></h3><h4>Multifaceted Applications of Wake Detection</h4><p>Ship wake detection serves as a cornerstone in a multitude of domains that are critical to both human and environmental well-being:</p><p><strong>Navigational Safety</strong>: Understanding ship wakes can provide insights into water currents and traffic patterns. This is vital for ensuring the safe passage of marine vessels, particularly in narrow straits and busy ports.</p><p><strong>Environmental Monitoring</strong>: The study of ship wakes can reveal the influence of vessels on aquatic ecosystems. For instance, excessive wake turbulence can lead to coastal erosion and can disrupt marine habitats.</p><p><strong>Maritime Surveillance</strong>: Wake detection plays a crucial role in maintaining maritime security. Tracking the wakes of vessels can help in identifying illegal activities such as smuggling or unauthorized fishing.</p><h3><strong>Specifications</strong></h3><ul><li><strong>Data Source</strong>: Sentinel-2 L2A</li><li><strong>Region of Interest</strong>: Danish fjords</li><li><strong>Classes</strong>: Wake, No-Wake</li><li><strong>Number of Samples</strong>:<ul><li>Wake: 123</li><li>No-Wake: 150</li></ul></li><li><strong>Spectral Bands</strong>: B2 (Blue), B3 (Green), B4 (Red), B8 (NIR)</li><li><strong>Post-Processing</strong>: CLAHE (Clip Limit = 0.12, Tile Size = 16x16)</li><li><strong>Average Wake Chip Size</strong>: 390x351 pixels</li><li><strong>Average No-Wake Chip Size</strong>: 380x390 pixels</li></ul><h3><strong>Wake Detection and Analysis</strong></h3><h4>Traditional Methods and Their Limitations</h4><p>Traditionally, the process of ship wake detection has largely been a manual endeavor or employed simplistic statistical algorithms. Analysts would sift through satellite or aerial images to identify ship wakes, a process that is both time-consuming and prone to human error. Even automated statistical methods often lack the robustness needed to differentiate between true wakes and false positives, such as aquatic plants or natural water disturbances.</p><h4>Role of xAI (Explainable AI) in Wake Identification</h4><p>The introduction of explainable AI (xAI) techniques brings another layer of sophistication to wake analysis. While traditional machine learning models may offer high performance, they often act as "black boxes," making it difficult to understand how they arrive at a certain conclusion. In a critical domain like navigational safety or maritime surveillance, the ability to interpret and understand model decisions is indispensable. xAI methods can make these machine learning models more transparent, providing insights into their decision-making processes, which in turn can aid in fine-tuning or fully trusting the models.</p><h4>Spectral Bands Selected</h4><p>The inclusion of four key spectral bands—B2, B3, B4, and B8—offers the scope for multi-spectral analysis. Different bands can capture varying features of water and wake textures, thereby offering a richer feature set for machine learning models. We use these spectral bands as referred to in [Liu, Yingfei, Jun Zhao, and Yan Qin. "A novel technique for ship wake detection from optical images." <i>Remote Sensing of Environment</i> 258 (2021): 112375.] </p><h4>Understanding Optical vs. SAR Imaging Modalities</h4><p>It is important to note the fundamental differences between wakes captured in Synthetic Aperture Radar (SAR) images and those in optical imagery. In SAR images, narrow-V wakes often arise due to Bragg scattering, a phenomenon that does not exist at optical wavelengths. In optical images, bright lines close to turbulent wakes are actually foams generated by the interaction between the surface horizontal flow of turbulent wakes and the surrounding background waves (Ermakov et al., 2014; Milgram et al., 1993; Peltzer et al., 1992). This can make the detection of wakes in optical images more challenging as there are usually no bright lines near turbulent wakes, and Kelvin arms may also show dark contrast. Methods that solely rely on searching for a trough and peak pair, taking the trough as the turbulent wake, would miss many actual wakes and could also result in the identification of false wakes.</p><h4>Contrast Enhancement</h4><p>The application of the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm to this dataset allows for enhanced local contrast, enabling subtle features to become more pronounced. This significantly aids machine learning algorithms in feature extraction, thereby improving their ability to distinguish between complex patterns.</p><h4>Environment and Clutter Assessment</h4><p>In addition to wakes, the dataset contains samples labeled as "No-Wake," which include environmental clutter and clouds. These samples are crucial for training robust models that can differentiate wakes from similar-looking natural phenomena.</p>
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