Aiming to analyze the influence of earth-atmosphere radiation on imaging characteristics of space object. A scene of space object motion and detection was designed by Satellite Tool Kit (STK) where visible light imagers mounted on geosynchronous earth orbit (GEO)/medium earth orbit (MEO) satellites were treated as observation platform, highly elliptical orbit (HEO) satellite was treated as object. Equivalent magnitude models of space object and earth-atmosphere radiation, and formulation for signal-to-noise ratio (SNR) of space object were derived by adopting infinitesimal method, according to spatial relationship between space object, earth, sun, and observation platform. The variation of equivalent magnitude between object and earth-atmosphere radiation, as well as the SNR were analyzed when tracking detector and gazing detector were arranged on observation platform. Simulation results indicate that the SNR of object on low orbit observation platform is higher than that on high orbit observation platform, the SNR of the former is 1.1 orders of magnitude higher than the latter on average, while the average imaging SNR of the latter is 1.9. Tracking detector’s object SNR is higher than gazing detector, the difference is largest when object enters or leaves detecting field of view, yet it is the smallest when object is close to detecting field of view. Moreover, the value of SNR obtained by simulation provides a guidance for the detection and recognition of space object, as well as a way of reduction of earth-atmosphere radiation.
A new background estimation and suppression algorithm was presented. In the algorithm, targets and observing noises
were considered as mixed interferences of the image background. With this situation, image background was estimated
adaptively and then background suppression was done in order to improve the signal-to-noise ratio (SNR) of targets. In
this algorithm, firstly, a Zernike-facet model of image background was built up. Secondly, the total least squares (TLS)
method was used to solve parameters of the model. Finally, background estimation and suppression were done using the
model and its parameters. Simulations and several experiments demonstrating the effectiveness of this proposed
algorithm were reported. And results show that this algorithm can be effective to estimate background in mixed noise
environment and can preserve detail information of targets and improve SNR of targets. As a result, detecting probability
and false probability will be improved in next process for automatic target detection and tracking.
Small and dim targets detection in the presence of strong background clutter is a challenging problem faced in many
applications including space surveillance and missile tracking. To solve this problem, a new fusion detection algorithm
applied image neighborhood entropy and univalue segment assimilating nucleus (USAN) principle is presented. In this
method the neighborhood entropy is used to locate small and dim targets. And the USAN principle is used to extract
some geometry features of targets including edges and inflexions. Based on these results, image fusion method is used to
detect real targets from noise and false targets. Finally, an iterative image threshold technique is proposed to label and
locate targets more precisely. Simulations and experiments show that the new fusion detection algorithm takes advantage
of the USAN principle and the neighborhood entropy method and it can detect small dim targets robustly, fast and
efficiently.
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