Sunggyun Im  Youngbae Jeon  Junghwan Hwang  Jiwon Yoon

10.5626/JOK.2019.46.8.823

satellite image Deep Learning image processing image contrast fusion Optimization

The satellite has various missions such as ground/marine observation, communication, broadcasting, etc. Satellite photographs provide information for the maintenance of marine security and traffic control for ship detection. Since satellite photos are taken all over the earth, the memory storage is not sufficient to hold such data with each data being of a high resolution and requiring automatic ship detection using the computer. The existing literature on ship detection employed several deep learning models. However, the problem of processing speed due to the characteristics of satellite photographs leads to the necessity of using a CNN(Convolution Neural Network) model that has a comparably high processing speed. On the contrary, it is difficult to improve the accuracy and performance mostly due to factors such as marina, lighthouses and waves. Therefore, in this paper, we propose a model that improves the accuracy and performance by combining image contrast enhancement with the existing CNN. In addition, we have employed the overlap and rotation functions to increase the amount of data required for ship classification in the learning stage and implement automation detection technology considering window sliding to reduce detection speed in real satellite photographs. Also, the identified ship data has been used as learning data to improve accuracy for the model that can be used in the real industry.