Multi-source Fusion Of Ship Targets Recognition Methods Based On Generative Adversarial Networks

With the advancement of naval ships in the modern military and economy,research on the identification technology of ship targets has become more and more important.There are various methods of target detection for marine vessels,including optical imaging,infrared imaging,radar imaging,etc.The multi-source imaging fusion recognition of ships has also become a research hotspot.In actual situations,the acquired multi-source imaging will be affected external conditions,and limited by its own sensors,resulting in poor imaging results.For example,the optical images resolution are high,but they are easily affected by the light source,weather,and climate,and some information is missing.Although radar imaging such as ISAR has a low resolution,it can achieve active detection,and its resolution characteristics are not affected by distance,so it can work all time and all weather.The resolution of infrared imaging is generally between the ISAR and optical imaging,it is a passive imaging system,and can also achieve all-weather imaging work.Based on the complementary and heterogeneous information of these three images,fusion recognition can be performed.First of all,this subject needs to obtain the ship’s optical images,ISAR images and infrared images data sets.The imaging simulation method is adopted.By building a 3D model of the ship and meshing it,the algorithm is used to simulate the ship targets imaging,finally image preprocessing is performed to construct images data sets.The results of the experiment provide basic data for subsequent processing.Secondly,with the Generative Adversial Networks(GAN)as the entry point,the improved GAN is used to improve the image quality of the ship.It mainly includes three algorithms: The optical imaging of the ship’s target is blocked by clouds and fog,which causes problems such as missing part of the ship’s image.The Pix2 pix network can improve the quality of the optical images and improve the target recognition rate of the ship,by training the optical images which covered cloud and fog and generating images;Based on the optical images and the ISAR images have complementary and heterogeneous information,and the ship images actually obtained has a problem of angle dismatch.The Cycle GAN is used to solve the problem of conversion and fusion of ship heterogeneous images,and it can alleviate the impact of small angle disturbances.The pix2 pix HD is an improved algorithm based on pix2 pix,which not only effectively improves the resolution of the generated images,but also can add a local generator to obtain high-definition images of the desired resolution.Therefore,the network is used to generate low-resolution infrared images into high-resolution shipoptical images.Finally,in order to further extract image features and remove redundant information,a ship multi-source fusion algorithm based on wavelet transform is used to generate new images.In order to judge the quality of the image generated based on the GAN,the improved Convolutional Neural Network(CNN)automatically extracts features,GAN’s input and generated images,fusion images,input them into the CNN,and compares and analyzes the recognition rates.The experimental results show that the method of this subject can not only automatically perform multi-source fusion,reduce the human and material resources consumed by manual extraction of features,but also effectively improve the recognition effect of ship targets.