SATELLITE IMAGE-BASED SHIP CLASSIFICATION METHOD WITH SENTINEL-1 IW MODE DATA

Abstract

Classification of a ship based on satellite imagery usually results differently depending on the type of polarization used in image generation. Also, the different ship’s orientation in each image degrades the performance of image-based classification. Given these points, Sentinel-1 data also needs some methods to classify the type of ship. For the classification, we have produced a ship dataset, KIOST-OpenSARShip, which was modified from the OpenSARShip dataset. We compared the brightness of each pixel of ship images generated by different polarizations. Based on this, we created a new image dataset. Then, we increased the similarity between ship images of the same type by aligning the heading direction in the ship images. As a result, our new datasets improve classification performances in some cases compared to using the OpenSARShip. The results of composite images from the VV- and VH-polarized images show up to 19.34% higher accuracy than those using only the one polarized images. In the future, we will improve the performance of the ship classification method considering various characteristics of the ship.