The aim of this project is to build a model using Deep Learning to automatically identify whether a remotely sensed target is a ship or not. Ship detection from remote sensing imagery is a crucial application for maritime security which includes traffic surveillance, protection against illegal fisheries, oil discharge control and sea pollution monitoring. Ship detection is typically done through the use of an Automated Identification System (AIS), which uses VHF radio frequencies to wirelessly broadcast the ship’s location, destination and identity to nearby receiver devices on other ships and land based systems. This is typically done through the use of an Automated Identification System (AIS). AIS are very effective at monitoring ships but fail to detect those which are not, and those which disconnect their transponder. Satellite imagery provides unique insights into various markets, including agriculture, defense and intelligence, energy, and finance. New commercial imagery providers, such as Planet, are using constellations of small satellites to capture images of the entire Earth every day. This flood of new imagery is outgrowing the ability for organizations to manually look at each image that gets captured, and there is a need for machine learning and computer vision algorithms to help automate the analysis process. The goal is to help address the difficult task of detecting the large ships in satellite images using deep learning model. Automating this process can be applied to many issues including monitoring port activity levels, monitoring illegal activities and supply chain analysis.
The dataset can be obtained from the below link https://www.kaggle.com/rhammell/ships-in-satellite-imagery