A Space Target Detection Method Based on Spatial–Temporal Local Registration in Complicated Backgrounds

Su, Yueqi; Chen, Xin; Cang, Chen; Li, Fenghong; Rao, Peng

doi:10.3390/rs16040669

Cited by 3 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors also explored different technologies for data analysis, including machine learning, genetic algorithms, and recurrent neural networks. They concluded that weather conditions, illumination variability, and dynamic background scenes could affect the results of the detection algorithm [12].…”

Section: Related Workmentioning

confidence: 99%

A Road Behavior Pattern-Detection Model in Querétaro City Streets by the Use of Shape Descriptors

Trejo-Morales,

Jimenez-Hernandez

2024

ASI

View full text Add to dashboard Cite

In this research, a proposed model aims to automatically identify patterns of spatial and temporal behavior of moving objects in video sequences. The moving objects are analyzed and characterized based on their shape and observable attributes in displacement. To quantify the moving objects over time and form a homogeneous database, a set of shape descriptors is introduced. Geometric measurements of shape, contrast, and connectedness are used to represent each moving object. The proposal uses Granger’s theory to find causal relationships from the history of each moving object stored in a database. The model is tested in two scenarios; the first is a public database, and the second scenario uses a proprietary database from a real scenario. The results show an average accuracy value of 78% in the detection of atypical behaviors in positive and negative dependence relationships.

show abstract

Section: Related Workmentioning

confidence: 99%

A Road Behavior Pattern-Detection Model in Querétaro City Streets by the Use of Shape Descriptors

Trejo-Morales,

Jimenez-Hernandez

2024

ASI

View full text Add to dashboard Cite

show abstract

Sub-pixel target fine spatial feature extraction method based on aperture coding and micro-scanning imaging mechanism

Zhang,

yuan,

Wang

et al. 2024

Opt. Express

View full text Add to dashboard Cite

The small imaging size of targets over long distances results in the loss of geometry and spatial features. Current methods are subject to sampling limitations and cannot accurately capture the spatial features of sub-pixel targets. This paper proposes a method to accurately locate and extract the fine spatial features of sub-pixel targets through aperture coding and micro-scanning imaging. First, the formation mechanism of imaging features for sub-pixel targets is analyzed. Second, the optical aperture is anisotropically coded in different directions to modulate the spreading spots of the target. The primary spreading direction and the center of the anisotropic spreading spots are extracted. The contour and the location of the target are determined from the spreading length and the intersections of the primary spreading directions. Then, the target is sampled by different detector units through various micro-scanning offsets. The pixel units containing different sub-pixel components of the target after offset are determined based on the location results. The fine spatial distribution of the sub-pixel target is reconstructed based on the intensity variations in the pixel units containing the target. Finally, the accuracy of the sub-pixel target fine spatial feature extraction method is validated. The results show a sub-pixel localization error of less than 0.02 and an effective improvement of the sub-pixel target spatial resolution. This paper provides significant potential for improving the ability to capture spatial features of targets over long distances.

show abstract

An Improved Multi-Target Tracking Method for Space-Based Optoelectronic Systems

Zhu,

Fu,

Wen

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Under space-based observation conditions, targets are subject to a large number of stars, clutter, false alarms, and other interferences, which can significantly impact the traditional Gaussian mixture probability hypothesis density (GM-PHD) filtering method, leading to tracking biases. To enhance the capability of the traditional GM-PHD method for multi-target tracking in space-based platform observation scenarios, in this article, we propose a GM-PHD algorithm based on spatio-temporal pipeline filtering and enhance the conventional spatio-temporal pipeline filtering method. The proposed algorithm incorporates two key enhancements: firstly, by adaptively adjusting the pipeline’s central position through target state prediction, it ensures continuous target tracking while eliminating noise; secondly, by computing trajectory similarity to distinguish stars from targets, it effectively mitigates stellar interference in target tracking. The proposed algorithm realizes a more accurate estimation of the target by constructing a target state pipeline using the time series and correlating multiple frames of data to achieve a smaller optimal sub-pattern assignment (OSPA) distance and a higher tracking accuracy compared with the traditional algorithm. Through simulations and real-world data validation, the algorithm showcased its capability for multi-target tracking in a space-based context, outperforming traditional methods and effectively addressing the challenge of stellar interference in space-based multi-target tracking.

show abstract

A Space Target Detection Method Based on Spatial–Temporal Local Registration in Complicated Backgrounds

Cited by 3 publications

References 40 publications

A Road Behavior Pattern-Detection Model in Querétaro City Streets by the Use of Shape Descriptors

A Road Behavior Pattern-Detection Model in Querétaro City Streets by the Use of Shape Descriptors

Sub-pixel target fine spatial feature extraction method based on aperture coding and micro-scanning imaging mechanism

An Improved Multi-Target Tracking Method for Space-Based Optoelectronic Systems

Contact Info

Product

Resources

About