Contour Detection of Disk Resolved Objects in Cassini ISS Image Using Deep Neural Network

In the astrometry of CCD image, it is important to match image stars with catalogue stars to correct the camera's pointing. The onboard Imaging Science Subsystem (ISS) in Cassini orbiter has taken a large number of images of targets which are disk resolved. In the astrometry of these images, the false image stars are often detected in the disk. It disturbs the pointing correction of camera and decline the precision of the astrometry. Therefore, it is helpful to find the contours of the disk to remove the false image stars. One method based on deep learning is proposed to detect the contour of disk resolved object in ISS images. A convolutional neural network was set up by the framework TensorFlow, in which the input is the nine features of each pixel, and the output is the classification of each pixel: contour pixel or non-contour pixel. The neural network is trained by about 36000 pixels, and then it is used to detect the contour pixels in 380 ISS images. Compared with the contour pixels labeled by hand, the contour pixels detected by neural network have the precision of 78.26% and the recall ratio of 73.32%. It proved that the proposed method is available to find the contour of disk resolved target in ISS images.