A NEW DEEP NEURAL NETWORK FOR OPTICAL AND SAR IMAGE FUSION

Alert or monitoring, runs through thousands of years of human history. Now, with the growth of the number of satellites in orbit, hundreds of terabytes of data are transmitted from the satellite to the data center every day. How to efficiently understand the information contained in these huge data in the face of practical needs is an increasingly urgent engineering challenge. However, most current computer vision methods are used for Optical images. Due to the presence of domain gaps between optical images and SAR images,the processing results are not ideal when Optical and SAR images are mixed. Therefore, in view of the above problems, a network model is proposed to realize the correlation between SAR images and Optical images. The model solves the problem that the imaging mechanism of SAR images differs from Optical images. The domain gaps cause SAR images are not directly used in Optical images computer vision method. This paper proposes an initial set of methods and models that have learned robust representations for Optical and SAR images dataset. So image analysts are able to interchangeably use Optical and SAR images for downstream tasks by using our models.