“…5 , background stars on a star chart often form excellent point sources in images. The spread function is based on the fact that the intensity gradient differential around stars is approximately zero, and it has the form 20 where is the asymmetry factor, is the local stop function, is the enhancement factor, is the local average intensity, and is the average background intensity of the whole image. Figure 5 Ideal stellar point source.…”
Section: Improved Target Detection With Multistage Hypothesis Testingmentioning
The detection of faint and small targets by space-based surveillance systems is difficult owing to the long distances, low energies, high speeds, high false alarm rates, and low algorithmic efficiencies involved in the process. To improve space object detection and help prevent collisions with critical facilities such as satellites, this study proposes an improved method for the detection of faint and small space-based targets. The proposed method consists of two components: star atlas preprocessing and space-based target detection. The star atlas preprocessing step applies multi-exposure image pyramidal weighted fusion to the original image containing the faint and small space-based target. After obtaining the image pyramidal weighted fusion result atlas, the algorithm employs threshold segmentation to improve the overall image clarity, highlight image details, and provide additional information for target detection. The detection of targets partially relies on the local symmetry of the image. Accordingly, a diffusion function describing the local symmetry is established to precisely locate stars by measuring the symmetry factor in a small area surrounding each pixel in the star atlas. This effectively removes the background stars while retaining high-definition and high-contrast images. The efficacy of the algorithm is validated using simulated datasets consisting of space-based and real images. The results demonstrate that the proposed technique improves the applicability of the multistage hypothesis testing (MHT) method in the context of a complex space environment, thus improving the performance of the space-based electro-optical detection system to better catalogue, identify, and track space targets.
“…5 , background stars on a star chart often form excellent point sources in images. The spread function is based on the fact that the intensity gradient differential around stars is approximately zero, and it has the form 20 where is the asymmetry factor, is the local stop function, is the enhancement factor, is the local average intensity, and is the average background intensity of the whole image. Figure 5 Ideal stellar point source.…”
Section: Improved Target Detection With Multistage Hypothesis Testingmentioning
The detection of faint and small targets by space-based surveillance systems is difficult owing to the long distances, low energies, high speeds, high false alarm rates, and low algorithmic efficiencies involved in the process. To improve space object detection and help prevent collisions with critical facilities such as satellites, this study proposes an improved method for the detection of faint and small space-based targets. The proposed method consists of two components: star atlas preprocessing and space-based target detection. The star atlas preprocessing step applies multi-exposure image pyramidal weighted fusion to the original image containing the faint and small space-based target. After obtaining the image pyramidal weighted fusion result atlas, the algorithm employs threshold segmentation to improve the overall image clarity, highlight image details, and provide additional information for target detection. The detection of targets partially relies on the local symmetry of the image. Accordingly, a diffusion function describing the local symmetry is established to precisely locate stars by measuring the symmetry factor in a small area surrounding each pixel in the star atlas. This effectively removes the background stars while retaining high-definition and high-contrast images. The efficacy of the algorithm is validated using simulated datasets consisting of space-based and real images. The results demonstrate that the proposed technique improves the applicability of the multistage hypothesis testing (MHT) method in the context of a complex space environment, thus improving the performance of the space-based electro-optical detection system to better catalogue, identify, and track space targets.
“…In order to resolve the aforementioned problems, this article proposes a target tracking algorithm based on image preprocessing and transformer [16]. First, the original image is pre-processed using a two-dimensional Gaussian soft thresholding method based on the denoising factor [17] to eliminate background noise, and the image is enhanced using a Laplace operator weighted fusion method after noise reduction [18,19]. Secondly, SiamCAR [20] is used as the overall framework of the target tracking algorithm, given that SiamCAR employs an anchorless bounding box based regression strategy for target state estimation go.…”
The target tracking by space-based surveillance systems is difficult due to the long distances, weak energies, fast speeds, high false alarm rates, and low algorithmic efficiencies involved in the process. To mitigate the impact of these difficulties, this article proposes a target tracking algorithm based on image processing and Transformer, which employs a two-dimensional Gaussian soft-thresholding method to reduce the image noise, and combines a Laplace operator-weighted fusion method to augment the image, so as to improve the overall quality of the image and increase the accuracy of target tracking. Based on the SiamCAR framework, the Transformer model in the field of natural language processing is introduced, which can be used to enhance the image features extracted from the backbone network by mining the rich temporal information between the initial and dynamic templates. In order to capture the information of the target’s appearance change in the temporal sequence, a template update branch is introduced at the input of the algorithm, which realizes the dynamic update of the templates by constructing a template memory pool, and selecting the best templates for the candidate templates in the memory pool using the cosine similarity-based selection, thus ensuring the robustness of the tracking algorithm. The experimental results that compared with the SiamCAR algorithm and the mainstream algorithms, the TrD-Siam algorithm proposed in this article effectively improves the tracking success rate and accuracy, addressing poor target tracking performance under space-based conditions, and has a good value of application in the field of optoelectronic detection.
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