Ship Detection and Classification in EO/IR VHR Satellite Imagery

Ship detection and classification in very high resolution (VHR) EO/IR satellite imagery, as primary objectives, were investigated using multiple techniques. Automated algorithms were developed and their performance was evaluated using different satellite image sources (Pleiades, WorldView-2/3). Performance of ship detection algorithms based on traditional (thresholding and saliency) techniques reached probability of detection 80% for low false alarm rates. Deep learning techniques based on convolutional neural networks (CNNs) (YOLOv4 and Mask R-CNN) achieved average precision of 94–95% with 3% of false positives without the need of accurate land and cloud masking. Mask R-CNN also allows accurate determining ship size parameters. The problem of ship and non-ship classification was investigated using traditional and CNN based techniques. Linear Discriminant Analysis, Support Vector Machines and combined classifiers achieved classification accuracies close to 80–90%. At the same time, the usage of a technique based on GoogleNet CNN achieved 99% classification accuracy for ship, small boats and background targets.