The YOLO network has been extensively employed in the field of ship detection in optical images. However, the YOLO model rarely considers the global and local relationships in the input image, which limits the final target prediction performance to a certain extent, especially for small ship targets. To address this problem, we propose a novel small ship detection method, which improves the detection accuracy compared with the YOLO-based network architecture and does not increase the amount of computation significantly. Specifically, attention mechanisms in spatial and channel dimensions are proposed to adaptively assign the importance of features in different scales. Moreover, in order to improve the training efficiency and detection accuracy, a new loss function is employed to constrain the detection step, which enables the detector to learn the shape of the ship target more efficiently. The experimental results on a public and high-quality ship dataset indicate that our method realizes state-of-the-art performance in comparison with several widely used advanced approaches.

PAG-YOLO: A Portable Attention-Guided YOLO Network for Small Ship Detection

With the continuous increase in the resolution of optical remote sensing satellites, the influence of space variations on the image quality cannot be ignored, especially in new imaging systems such as thin-film diffraction and rectangular rotating pupils. This letter was conducted to analyze the influence of space variability on restoration performances of different methods, and then, a new processing strategy of space-variant images is proposed. According to the analytical experiment results, we suggest using the block method when the pixel space variability (PSV) < 0.20% and, otherwise, selecting the global method. In order to ensure the final image quality, we also suggest controlling the PSV within 0.28% when designing optical systems. This study can provide a foundation for optimizing the design of front-end optical systems and selecting back-end processing methods in engineering applications.

Influence of Space Variability on Remote Sensing Image Restoration Performances

Thin-film diffractive optical elements (DOEs) have considerable potential to be used in the field of high-resolution remote sensing imaging satellites because of advantages such as a large aperture, small volume, lightness, wide tolerance range of surface shape, and easy replication. However, there are problems associated with thin-film diffraction imaging, including space variation, serious blur, and low contrast, which result in insufficient imaging quality with regard to traditional optical system requirements. To address this, a local adaptive prior-based image restoration method is proposed for thin-film diffraction imaging systems. An entire degraded image was divided into several isohalo regions based on imaging characteristics. Then, the regularization constraints were adaptively selected and updated according to the local scene prior characteristics. Additionally, the system parameters in the corresponding field of view were used as input to restore each subregion. In particular, the diffraction efficiency (DIE) was introduced into the model to remove the nondesign level background radiation. The experimental results show that the proposed algorithm can effectively improve the image quality of a thin-film diffraction imaging system, including space variation correction, clarity enhancement, and background radiation suppression. Furthermore, a DIE of less than 60% was found to significantly impact the final image products. 

Local Adaptive Prior-Based Image Restoration Method for Space Diffraction Imaging Systems

Large field circular scanning system obtains remote sensing images by rotating scanning, which is one of the technical ways to effectively expand the field of view of geostationary orbit optical remote sensing imaging system. However, due to its special rotation scanning imaging mechanism, the on-orbit imaging process of this system is affected by the coupling of detector rotation sampling, satellite platform vibration, optical system aberration and other factors, so the image degradation characteristics are very complex. In this paper, the on-orbit imaging characteristics of large field circular scanning system are first analyzed to provide a theoretical basis for the subsequent research of image restoration method. Secondly, aiming at the problems of annular geometric distortion and spatial blur of the system image, the image restoration method is proposed based on ring block and regularization methodology. Experimental results show that under the condition of detector rotation speed ω

Imaging characteristics and image restoration method for large field circular scanning system

Thin-film diffractive optical elements (DOEs) have considerable potential to be used in the field of high-resolution remote sensing imaging satellites because of advantages such as a large aperture, small volume, lightness, wide tolerance range of surface shape, and easy replication. However, there are problems associated with thin-film diffraction imaging, including space variation, serious blur, and low contrast, which result in insufficient imaging quality with regard to traditional optical system requirements. To address this, a local adaptive prior-based image restoration method is proposed for thin-film diffraction imaging systems. An entire degraded image was divided into several isohalo regions based on imaging characteristics. Then, the regularization constraints were adaptively selected and updated according to the local scene prior characteristics. Additionally, the system parameters in the corresponding field of view were used as input to restore each subregion. In particular, the diffraction efficiency (DIE) was introduced into the model to remove the nondesign level background radiation. The experimental results show that the proposed algorithm can effectively improve the image quality of a thin-film diffraction imaging system, including space variation correction, clarity enhancement, and background radiation suppression. Furthermore, a DIE of less than 60% was found to significantly impact the final image products.

Diffractive membrane imaging systems have been an important development trend for high-orbit satellite cameras owing to their advantages of large aperture, light weight, rapid manufacture, and low cost. However, caused by the cross-coupling effects of diffraction imaging, membrane properties, subaperture stitching, on-orbit disturbances, and other physical factors, lager-aperture space diffractive membrane imaging systems have specific and complex degradation characteristics: the modulation transfer function (MTF) and signal-to-noise ratio (SNR) have more prominent degradation and serious space-variant characteristics over fields of view, with obvious background radiation properties that seriously affect the application of imaging products. To address this problem, this study established a global information transmission model by characterizing the PSF and background radiation in a full field of view to represent the imaging law of an on-orbit system. Aiming at the inverse problem of the information transmission model, we also propose a novel image inversion restoration method for the special degradation characteristics. In particular, the effect of diffraction efficiency is introduced into the inversion restoration method to solve the background radiation problem. Moreover, we innovatively designed matrix regularization parameters to further improve the correction ability of spatial variation. When the diffraction efficiency was experimentally higher than 60% and the mean measured spatial variability was less than 0.2, the proposed method exhibited a satisfactory processing performance, and could improve multiobjective comprehensive processing, such as transfer function compensation, spatial variation correction, and background radiation removal.

Chao Tian

Papers

Global Information Transmission Model-Based Multiobjective Image Inversion Restoration Method for Space Diffractive Membrane Imaging Systems