Space debris detection is important in space situation awareness and space asset protection. In this article, we propose a method to detect space debris using feature learning of candidate regions. The acquired optical image sequences are first processed to remove hot pixels and flicker noise, and the nonuniform background information is removed by the proposed one dimensional mean iteration method. Then, the feature learning of candidate regions (FLCR) method is proposed to extract the candidate regions and to detect space debris. The candidate regions of space debris are precisely extracted, and then classified by a trained deep learning network. The feature learning model is trained using a large number of simulated space debris with different signal to noise ratios (SNRs) and motion parameters, instead of using real space debris, which make it difficult to extract a sufficient number of real space debris with diverse parameters in optical image sequences. Finally, the candidate regions are precisely placed in the optical image sequences. The experiment is performed using the simulated data and acquired image sequences. The results show that the proposed method has good performance when estimating and removing background, and it can detect low SNR space debris with high detection probability.

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Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

A wide-field surveillance system with a long exposure time has a stronger detection capability for faint space targets. However, some of the complexities it generates also pose difficulties for spa...

Space target detection in optical image sequences for wide-field surveillance

This paper develops smoothing data association based on integrated probabilistic data association (FLIPDA-S) tracker to identify a multicopter UAV (MUAV) and estimate its trajectory in an uncertain and cluttered environment. Recently, a number of unidentified MUAVs have been flown in prohibited airspace and a radar system has been utilized to identify the MUAV and track the trajectory. However, target tracking methods could have difficulties to track the target MUAV through randomly distributed and moving objects. The vehicle state estimation (VSE) algorithm, which utilize FLIPDA-S in this paper, adopts UAV kinematics and dynamics for tracking a UAV in clutter and false targets with significant UAV identification performance. The performance of the method has been validated with and without an initial position of the target MUAV. Both numerical simulation and experiments are demonstrated to verify the effectiveness and accuracy of FLIPDA-S with VSE algorithm to track UAVs in an uncertain and clutter environment.

Dynamic based trajectory estimation and tracking in an uncertain environment

Considering that large quantities of soil hyperspectral data include the redundancy and overlap of spectral information, the technology of selecting feature wavelength can effectively solve these problems, and improve the accuracy and stability of the soil organic matter (SOM) content retrieval model. Traditional methods of wavelength selection mainly attempt to establish the empirical relationship between reflectance and SOM contents, and the performance is directly related to the quality and representativeness of the “training data”. This study first distinguished the sensitive wavelength interval of SOM through the sensitivity analysis (SA) of the SOM to soil reflectance in radiative transfer model. Then sensitive wavelength points of SOM were ascertained using the successive projection algorithm (SPA): 468, 476, 496, 599, 775 and 900nm. Results show that SOM content can be estimated with high accuracy (root-mean-square error of prediction (RMSEP) $\text{R}^{2})>82.9$ %) by adopting the selected six wavelengths. Especially at 599nm, the accuracy of SOM content estimation is the highest (RMSEP: 0.176%, R2: 90.4%). Compared with traditional empirical wavelength selection methods, the wavelength selection based on the SA-SPA with the SOM radiative transfer model improves the generalizability and accuracy of the result. The research provides theoretical basis and technical support for the remote sensing retrieval of SOM, the development of rapid spectral instruments, and the bands setting of sensor instrument.

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Wavelength Selection for Estimating Soil Organic Matter Contents Through the Radiative Transfer Model

Abstract In the past few years, the increasing amount of space debris has triggered the demand for distributed surveillance systems. Long exposure time can effectively improve the target detection capability of the wide-area surveillance system. Problems that also cause difficulties in space-target detection include large amounts of data, countless star points, and discontinuous or nonlinear targets. In response to these problems, this paper proposes a high-precision space-target detection and tracking pipeline that aims to automatically detect debris data in space. First, a guided filter is used to effectively remove the stars and noise, then Hough transform is used to detect space debris, and finally Kalman filter is applied to track the space debris target. All experimental images are from Jilin Observatory, and the telescope is in star-tracking mode. Our method is practical and effective. The results show that the proposed automatic extraction channel of space debris can accurately detect and track space targets in a complex background. 

/pdf/automatic-extraction-channel-of-space-debris-based-on-wide-1ed7f8iu.pdf

Automatic extraction channel of space debris based on wide-field surveillance system

Long exposure time and wide field can effectively improve the ability of a space surveillance telescope to detect faint space targets. However, complicated situations pose challenges for space target detection. Background star images usually manifest a rotated streak, and target trajectories can be crossed, discontinuous, or nonlinear. This paper presents an accurate and robust space target detection method, namely, spatiotemporal pipeline multistage hypothesis testing (SPMHT), to overcome the issues. Specifically, the method includes the following two stages: First, in the spatiotemporal pipeline filtering step, Spatiotemporal-related Intersection over Union (SrIoU) is used to calculate the IoU score instead of the traditional method. Benefiting from the differences between motion characteristics of targets and stars and the insensitivity of the SrIoU score to the noise, the spatiotemporal pipeline filtering can effectively remove the streak images of background stars and obtain candidate points. Second, a series of candidate points is further organized into a tree structure. We pruned in the tree structure combined with these candidate trajectories by using velocity and direction feature of moving objects. Furthermore, in the search step, fast adaptive sequence region search is used to reduce the computational cost. The experimental results for two datasets, simulated image datasets and real captured image datasets, demonstrate the effectiveness in addressing the difficulties of space target detection in complicated situations.

/pdf/space-target-detection-in-complicated-situations-for-wide-4cmog4eltg.pdf

Space Target Detection in Complicated Situations for Wide-Field Surveillance

The wide-field surveillance camera plays a critical role in space debris detection and the visible light situational awareness required for early warning. However, stray light in such a system has always been a serious problem. Current methods cannot effectively eliminate the interference of stray light, which directly lead to the inability to accurately segment the target and background, which greatly reduce the accuracy of target recognition. To solve this problem, we proposed an accurate stray light elimination method based on recursion multi-scale gray-scale morphology (RMGM). First, we defined two structural operators with different domains. These two structural operators can make full use of the difference information between the target region and the surrounding background region, which is the basic premise to ensure high-precision correction. Then we used the two structural operators with different domains to perform morphological processing on the surveillance image to eliminate stray light. Finally, in order to ensure that targets with different sizes in the surveillance image are not lost, we adopt a recursion multi-scale method. We increase the size of structural operator and perform the morphological operation again on the estimated and eliminated stray light non-uniform background in order to retrieve the lost larger size target. In addition, we add an automatic decision mechanism on the recursion multi-scale method by using corresponding threshold judgment. Further experimental results on real captured image datasets show that compared with other methods, the proposed RMGM method can simultaneously have high-precision stray light elimination effect, high-precision target retention rate, and faster computation time.

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Stray Light Elimination Method Based on Recursion Multi-Scale Gray-Scale Morphology for Wide-Field Surveillance

Rapid and accurate monitoring of soil organic matter (SOM) content is of great significance for precision fertilization of farmland. However, the SOM retrieval models are mainly established by statistical methods, which have limited application scope and incomplete theoretical foundation. Moreover, the accuracy of the SOM retrieval models remains raised. In this paper, for the first time, a semi-empirical SOM content retrieval model is constructed, which has certain theoretical basis, strong applicability and higher accuracy than before. Based on the Kubelka-Munk (KM) theory, the SOM retrieval model with the absorption coefficient k and scattering coefficient s related to SOM (r = k/s) is derived. The validity and reliability of the model are confirmed with validation set (n = 26) including three sorts of soils. Results show that the model can estimate SOM content in different sorts of soils with high prediction accuracy and good prediction ability (root mean square errors of prediction (RMSEP), coefficients of determination (R
 2
) and relative percentage deviation (RPD) values of 0.18%, 89.9% and 3.2, respectively) in the range of 552-950nm. The model provides an innovative method for predicting SOM content.

/pdf/semi-empirical-soil-organic-matter-retrieval-model-with-4serr4dr4a.pdf

Chunhui Hu

Papers

Space Target Detection in Complicated Situations for Wide-Field Surveillance

Stray Light Elimination Method Based on Recursion Multi-Scale Gray-Scale Morphology for Wide-Field Surveillance

Semi-Empirical Soil Organic Matter Retrieval Model With Spectral Reflectance