Advancement in Satellite - Based Iceberg Detection: A Deep Learning Approach

Iceberg detection is essential for both marine safety and business operations in areas where ice formations are common. The use of satellite data for the automatic detection of icebergs has gained importance as high-quality satellite photography has been more widely available. When it comes to iceberg identification, satellite imagery is essential. Accurate identification remains challenging, especially when distinguishing icebergs from other things like ships. Existing iceberg detection models frequently identifies icebergs as ships, which can have disastrous results. The goal of this research is to create a technique for iceberg detection that makes use of high-resolution visible spectral images and satellite radar data. This proposed approach aims to improve the accuracy and efficiency of iceberg detection, which in turn will ensure the safety and effectiveness of navigation and economic activities in Arctic regions. Feature extraction, classification, training with validation, and evaluation are statistical criteria used to analyze two-dimensional fields of satellite images. Typically, satellite images are captured as two-dimensional representations of the Earth's surface, where each pixel in the image corresponds to a specific geographic location. In order to successfully categorize icebergs from satellite images, this study describes the creation of Convolutional Neural Network (cnn), vgg16 & resnet, and transfer learning techniques. With a focus on its potential importance for improving maritime safety stimulating economic growth in areas vulnerable to ice dangers, it provides a thorough explanation of the approach used.