# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
### Related Work and Motivations  
For VHR images, a large amount of literature exists, with the number of works following the increasing number of sensors and the quantity of publicly available data [7; 8]. Many of these approaches focused on detecting ships with classical image processing pipelines: image processing using spectral indices or histograms (e.g., sea-land segmentation, cloud removal), ship candidate extraction (e.g., threshold, anomaly detection, saliency), and, then, rule-based ship identification or classification using statistical methods. Virtually all of these works focus on VHR images with R,G,B, and PAN bands, occasionally with the addition of NIR, with resolution less than 5 m. Deep learning was applied to images with under 1m resolution by using object detection convolutional neural networks (CNN): R-CNNs [9; 10], YOLO [11; 12], U-Net [13; 14].  
\begin{table}
\begin{tabular}{l} \hline \hline Acquisition mode: Push-Broom \\ Resolution: 10 m (R,G,B, NIR), 20 m, 60 m (other bands) \\ Swath: 290 km \\ Revisit time: 5 days with 2 satellites \\ Spectral bands: 13, between 443 nm–2190 nm \\ Data production rate: 2 TB/day \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sentinel-2 satellite and capabilities.  
For SAR imagery, [1] reviews four operational ship detectors that work on multiple sensors. All of the approaches use classical processing chains and start by filtering out land pixels. This filter is either based on shapefiles or on land/water segmentation masks generated from the SAR image. However, in both cases, a large margin is taken around the coastlines, eliminating any ships that are moored in ports. Deep learning was also applied to SAR ship detection, with notable results detailed in [15].  
In multi-spectral images, the most notable work is [4] which uses SVMs to identify water, cloud, and land pixels and then builds a CNN to fuse multiple spectral channels. This fusion network predicts whether objects in the water are ships. Other approaches, such as [3], rely on hand made rules on size and spectral values to distinguish between ships, clouds, islands, and icebergs. The only Sentinel 2 ship dataset publicly available is [16] but it only includes small size image chips and weak annotations for precise localization, i.e., a single point for each ship, obtained by geo-referencing AIS GPS coordinates to pixel coordinates in the chips.  
Although large datasets exist for VHR images, for Sentinel-2 none are available with pixel level annotations while usually thousands of examples are needed to train deep learning object detectors. Few-shot learning based approaches can bring interesting perspectives for remote sensing in general and in our setting in particular. Few-shot learning consists in training a neural network with few labeled samples, most often thanks to quality feature extractors upon which transfer learning is performed. One recent method for unsupervised learning of features extractors that enable few-shot learning is contrastive self-supervised learning [6; 17]. Contrastive SSL relies on a \"pretext training task\", defined by the practitioner, that helps the network to learn invariances and latent patterns in the data [18; 19; 20].  
Several strategies exist for choosing the pretext task: context prediction [21], jigsaw puzzle, or simply by considering various augmented views. The latter is used by [22; 23] for remote sensing applications like land use classification and change detection.