With the increasing demand for the wide-area refined detection of aircraft targets, remote sensing cameras have adopted an ultra-large area-array detector as a new imaging mode to obtain broad width remote sensing images (RSIs) with higher resolution. However, this imaging technology introduces new special image degradation characteristics, especially the weak target energy and the low signal-to-noise ratio (SNR) of the image, which seriously affect the target detection capability. To address the aforementioned issues, we propose an aircraft detection method for RSIs with low SNR, termed L-SNR-YOLO. In particular, the backbone is built blending a swin-transformer and convolutional neural network (CNN), which obtains multiscale global and local RSI information to enhance the algorithm’s robustness. Moreover, we design an effective feature enhancement (EFE) block integrating the concept of nonlocal means filtering to make the aircraft features significant. In addition, we utilize a novel loss function to optimize the detection accuracy. The experimental results demonstrate that our L-SNR-YOLO achieves better detection performance in RSIs than several existing advanced methods.

With the rapid development of earth observation by optical remote sensing satellites, there is an urgent requirement of remote sensing data with high-resolution in environmental monitoring, urban planning and other applications. The resolution of remote sensing images (RSIs) significantly depends on the aperture size of space imaging system. However, limited by manufacturing level and carrying capacity, the traditional optical materials and reflection/refraction imaging principle have encountered a bottleneck in the manufacturing of space imaging system with ultra-large aperture and lightweight. Consequently, it is necessary to develop brand new space optical imaging systems. In this paper, we summarize the imaging mechanism and development history of three ultra-large aperture imaging technologies, including synthetic aperture imaging technology, diffractive membrane imaging technology and rotating synthetic aperture imaging technology, and analyze the challenges existing in their application.

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