Satellite Laser Ranging (SLR) technology is one of the most accurate geospatial measurement technologies at present, and has been widely used in the precision orbiting of satellites, determining the Earth's rotation parameters, establishing and maintaining the Earth's reference frame, as well as realizing high-precision time transfer on a global scale [[1], [2], [3], [4]]. With the continuous development of laser technology and the demand of research in various fields, most of the SLR observatories have realized the routine observation of kHz frequency, and the amount of observation data has been greatly increased [[5], [6], [7], [8]].

There are a large number of outliers in the raw SLR echo data. Although filtering techniques such as ultra-narrow band filters, narrowing of the receiving diaphragm, and distance gating have increased the signal-to-noise ratio of SLR systems, the noise magnitude in the echo data is still huge [9]. For this reason, SLR stations usually use manual screen processing methods to preprocess SLR observations and then combine them with the normal point process to generate SLR normal point data products [10,11]. Among them, the manual screen processing method as the first step in the data processing process, its processing results directly affect the difficulty and accuracy of the subsequent processing process. However, the method relies heavily on manual experience and is highly susceptible to subjective judgment. Meanwhile, with the continuous development of SLR system hardware, the amount of SLR observation data and transmission rate have increased substantially, and the manual judgment can no longer meet the application requirements of the future SLR system.

In order to enrich the research system of SLR data processing, researchers have fully explored and mapped out the algorithms of SLR data identification and filtering. Literature [12] proposes a Graz fast echo identification algorithm, where each data point in the SLR data is compared with 1000 data points in front of this point, and the difference is considered as an effective echo if the number of times the difference is greater than a threshold value within a certain band; Literature [13] proposed a correlation detection algorithm for the SLR2000 system, which grids the SLR data and compares the amount of data in each grid with a set threshold for the judgment of effective echoes; Literature [14] proposed a Poisson filtering algorithm using the Poisson process of the detector response as the design idea. The algorithm starts from the statistical distribution law of SLR echo photon number, and utilizes a rectangular window to perform slope scanning of SLR data over a short time interval and histogram statistics of the scanning results. When the number of echo points counted per unit time interval exceeds the threshold value and conforms to the Poisson process, it is determined that effective echo data exists in the region; An improved Poisson filtering algorithm has been proposed in the literature [15]. The algorithm is based on the analysis of SLR single-photon and multi-photon detection mechanisms, and uses the multi-photon detection equation to evaluate the number of photons at the time of the detector response during that time interval, and filters out the observations that do not conform to the single-photon detection and the noise at the same time, so as to realize the identification of effective echo data. In the above method, it is necessary to count the amount of SLR raw echo data in the time cell and compare it with a pre-set threshold. The setting of the threshold value is related to the shape and orbit of the satellite, the structural parameters of the SLR system and the observation environment, which makes the above algorithms less generalized and not applicable to the automatic identification and processing of SLR data.

In order to realize the automatic identification and processing of SLR data, we have studied the distribution characteristics of SLR data and found that the noise in SLR data is similar to the Salt-and-Pepper Noise (SPN) in images, and the image processing technology has the advantages of high efficiency, high accuracy and high automation degree. Therefore, we propose an automatic processing method for SLR data based on image denoising technology, which transforms SLR data into images and then denoises the images, and verify the effectiveness and applicability of the method through both theory and experiment to provide theoretical and technical support for realizing the automatic processing of massive observational data of high-frequency SLR systems.