Automatic Space Debris Extraction Channel Based on Large Field of view Photoelectric Detection System

Jiang, Ping; Liu, Chengzhi; Yang, Wenbo; Kang, Zhe; Li, Zhenwei

doi:10.1088/1538-3873/ac4c9d

Cited by 6 publications

(6 citation statements)

References 26 publications

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“…It is necessary for relevant algorithms to remove background stars firstly. Jiang et al [39] used IPI to extract faint space target after background stars suppression by multi-frame median filter. However, the limitation lead to poor effectiveness of these methods for space sources extraction in single image.…”

Section: Discussionmentioning

confidence: 99%

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A Robust Space Target Extraction Algorithm Based on Standardized Correlation Space Construction

Wang,

Chen,

Shen

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the increasing number of space debris in nearearth space, the surveillance of space has become more crucial to prevent potential space collisions. In an optical surveillance system, a multitude of debris with varying sizes, speeds and intensities exist within images, which brings great difficulties for the extraction of targets. Current methodologies mainly emphasize local saliency enhancement and source extraction based on global statistical attributes, yet often overlook the distribution similarity of individuals and how to map them in a standardized space. To address this limitation, our paper introduces an innovative transformation from grayscale space to correlation space. Initially, a fourfold-structure model is designed to measure the local correlation for each element in the grayscale space. Subsequently, the standardized correlation space is constructed by correlation measurement. Then, combining an adaptive threshold choosing method based on statistics and a threshold limitation strategy based on correlation, the individuals can be separated from the feature space. Finally, a deblending method is applied to disentangle merged individuals based on the local gradient correlation. Thoroughly assessed using ten subimage sequences featuring targets with typical traits against complex backgrounds, the results confirm its superior extraction capabilities and effectiveness when compared to ten common baseline methods, which achieves an average 90.9% true positive rate in just 2.8 ms. Furthermore, the extraction outcomes serve as a basis for detecting true target trajectories in continuous sequences. This integration holds significant implications for practical applications in space surveillance engineering.

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Section: Discussionmentioning

confidence: 99%

“…The utilization of median filters, Gaussian filters, and other techniques has been documented for noise suppression [16], [19], [35], [39]. Nonetheless, these methods can alter the original intensity distribution within merged regions, potentially leading to the blending of neighboring sources.…”

Section: Merged Sources Deblending Based On Gradient Correlationmentioning

confidence: 99%

A Robust Space Target Extraction Algorithm Based on Standardized Correlation Space Construction

Wang,

Chen,

Shen

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…It should be noted that the spatial targets discussed in this article include spatial stars and spatial moving targets. Therefore, based on the characteristics of spatial images, the following model is established [28]:…”

Section: Imaging Characteristicsmentioning

confidence: 99%

Non-Uniform Background Noise Suppression Method Based on Improved Total Variation Model for Wide-Field Imaging System

Zhou,

Wu,

Wang

et al. 2024

IEEE Access

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A wide-field surveillance system with a long exposure time has a stronger detectability for dim space targets. However, with the increase in exposure time and working temperature, complex non-uniform background noise containing hot pixels of the detector cannot be ignored, seriously affecting the background and imaging quality. This article studies and proposes a high-performance denoising method, which does not use any prior knowledge of the target and can automatically remove noise from the image. This method is based on an improved total variation model to remove hot pixels and other background mixed noise in widefield system images. Firstly, using the idea of the traditional local contrast method (LCM), we utilize the significant difference in grayscale values between contaminated pixels and neighboring pixels to detect impulse noise, such as the hot pixels in the image. And then, we designed an improved adaptive maximum filtering algorithm to remove unwanted contamination, which protected target information from being lost and optimized pixels that were attacked by impulse noise. Finally, the total variation algorithm is used to eliminate residual background noise, the detector's readout noise, and non-uniform response. The method proposed in this article can effectively filter out hot pixels and non-uniform background noise while preserving the details of target edges. We conducted experiments on a large number of simulated and original images. For star maps captured in long exposure mode, the method proposed in this article has obvious advantages over several competing algorithms. The experimental results show that, compared to competitive algorithms, the algorithm proposed in this article improves PSNR by at least 13.1%, SSIM by at least 0.4%, IEF by at least 5 times, and IQI by at least 9.2%. At the same time, the algorithm in this article achieved a moderate level of computation time.INDEX TERMS wide-field surveillance system; long exposure time; non-uniform correction; local contrast method; maximum filter; total variation

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“…In contrast, the satellite platform attitude variation increases the image background uncertainty and complexity, which makes the star map registration method unsuitable for space-based scenarios. Some threshold segmentation methods based on target enhancement have been studied, including the wavelet filtering method [13,14], local contrast method [15,16], and morphology filtering. Boccignone et al [13] presented a small target detection method using wavelets.…”

Section: Related Workmentioning

confidence: 99%

“…Boccignone et al [13] presented a small target detection method using wavelets. Jiang et al [14] improved this method and developed an automatic space debris extraction algorithm. It utilized wavelet transform and variational hybrid filtering algorithms to suppress noise and detected candidate debris targets using the Hough transform.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

Chen

Liu

et al. 2023

Remote Sensing

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Space-based target surveillance is important for aerospace safety. However, with the increasing complexity of the space environment, the stellar target and strong noise interference pose difficulties for space target detection. Simultaneously, it is hard to balance real-time processing with computational performance for the onboard processing platform owing to resource limitations. The heterogeneous multi-core architecture has corresponding processing capabilities, providing a hardware implementation platform with real-time and computational performance for space-based applications. This paper first developed a multi-stage joint detection and tracking model (MJDTM) for space targets in optical image sequences. This model combined an improved local contrast method and the Kalman filter to detect and track the potential targets and use differences in movement status to suppress the stellar targets. Then, a heterogeneous multi-core processing system based on a field-programmable gate array (FPGA) and digital signal processor (DSP) was established as the space-based image processing system. Finally, MJDTM was optimized and implemented on the above image processing system. The experiments conducted with simulated and actual image sequences examine the accuracy and efficiency of the MJDTM, which has a 95% detection probability while the false alarm rate is 10−4. According to the experimental results, the algorithm hardware implementation can detect targets in an image with 1024 × 1024 pixels in just 22.064 ms, which satisfies the real-time requirements of space-based surveillance.

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Automatic Space Debris Extraction Channel Based on Large Field of view Photoelectric Detection System

Cited by 6 publications

References 26 publications

A Robust Space Target Extraction Algorithm Based on Standardized Correlation Space Construction

A Robust Space Target Extraction Algorithm Based on Standardized Correlation Space Construction

Non-Uniform Background Noise Suppression Method Based on Improved Total Variation Model for Wide-Field Imaging System

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

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