RESUMEN
Daytime application of the pyramid wavefront sensor (PyWFS) is greatly challenged by a bright and fluctuating sky background, especially in the visible. A daytime-Py approach to apply visible pyramid wavefront sensing for real-time daylight AO is described in this paper. A field stop (FS) and a lenslet array are applied in the daylight AO system based on a visible PyWFS to separate the object signal from the background signal and improve the signal-to-noise ratio (SNR). A background elimination algorithm is proposed to extract the effective object signal. Closed-loop experiment using the daytime-Py approach is performed, which presents the first laboratory real-time daylight natural guide star AO correction of a faint object based on a visible PyWFS. SNR ranges for both the daytime-Py approach and PyWFS are reported. Furthermore, the correction results in different SNRs using both methods and with various pupil samplings using the daytime-Py approach are presented to prove that our proposal has the advantages over the PyWFS and Shack-Hartmann wavefront sensor (SHWFS) for daylight AO. This study demonstrates that the daytime-Py approach can realize the real-time object tracking and closed-loop correction in the daylight natural guide star adaptive optics (AO) system based on the visible PyWFS.
RESUMEN
Efficient ship detection is essential to the strategies of commerce and military. However, traditional ship detection methods have low detection efficiency and poor reliability due to uncertain conditions of the sea surface, such as the atmosphere, illumination, clouds and islands. Hence, in this study, a novel ship target automatic detection system based on a modified hypercomplex Flourier transform (MHFT) saliency model is proposed for spatial resolution of remote-sensing images. The method first utilizes visual saliency theory to effectively suppress sea surface interference. Then we use OTSU methods to extract regions of interest. After obtaining the candidate ship target regions, we get the candidate target using a method of ship target recognition based on ResNet framework. This method has better accuracy and better performance for the recognition of ship targets than other methods. The experimental results show that the proposed method not only accurately and effectively recognizes ship targets, but also is suitable for spatial resolution of remote-sensing images with complex backgrounds.