Satellite laser altimetry can obtain sub-meter or even centimeter-scale surface elevation data over large areas, but it is inevitably affected by scattering caused by clouds, aerosols, and other atmospheric particles. This laser ranging error caused by scattering cannot be ignored. In this study, we systematically combined existing atmospheric scattering identification technology used in satellite laser altimetry and observed that the traditional algorithm cannot effectively estimate the laser multiple scattering of the GaoFen-7 (GF-7) satellite. To solve this problem, we used data from the GF-7 satellite to analyze the importance of atmospheric scattering and propose an identification scheme for atmospheric scattering data over land and water areas. We also used a look-up table and a multi-layer perceptron (MLP) model to identify and correct atmospheric scattering, for which the availability of land and water data reached 16.67% and 26.09%, respectively. After correction using the MLP model, the availability of land and water data increased to 21% and 30%, respectively. These corrections mitigated the low identification accuracy due to atmospheric scattering, which is significant for facilitating satellite laser altimetry data processing.
In November 2019, the GaoFen-7(GF-7) satellite was equipped with China's first laser altimeter with full waveform recording capability, which obtains high-precision long-range three-dimensional coordinates. The influence of clouds is noticeable for laser transmission, and a footprint camera is used to determine laser pointing and to image the ground. However, the cloud inevitably appears in the laser footprint image. In this study, the authors propose a cloud detection scheme for footprint images based on deep learning. First, an adaptive pooling model is proposed according to the characteristics of the cloud region. Next, model fusion was performed based on the SegNet and U-Net training results. Finally, test time augmentation was used to enhance the data and to improve cloud detection accuracy. The experimental results show that the fusion result of the model was approximately 5% better than that of the traditional cloud detection algorithm, which improved the shortcomings of the traditional algorithm, such as poor detection effect for thin clouds and complex underlying cloud surfaces. The related conclusions have certain reference significance for GF-7 data processing and related research on footprint images.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
The GF-7 satellite is China’s first civil sub-meter resolution stereo mapping satellite, aiming at 1:10,000-scale mapping. To achieve this goal, apart from the stereo optical cameras that reach sub-meter resolution, the GF-7 satellite is equipped with a laser altimetry system capable of obtaining three-dimensional laser points (LPs) with high elevation accuracy. However, the combination of laser altimetry data and optical stereo images has not been thoroughly studied. In this paper, we exploit the images recorded by the highly integrated laser footprint cameras and propose a hierarchical phase correlation method based on a geographic pyramid for the registration of laser altimetry data and high-resolution optical stereo images, which lays a solid foundation for the following combined adjustment. Experiments show that the proposed registration method can automatically locate the LPs on high-resolution stereo images and meet the requirements of bundle adjustment. A series of bundle adjustment experiments were carried out, showing that laser altimetry data can significantly enhance the vertical accuracy of optical image stereo mapping and that elevation accuracy can reach roughly 1.0 m (RSME) without ground control points. Therefore, this study could be a good guide for global high-precision DSM acquisition with the GF-7 satellite.
The Gaofen-7 (GF-7) satellite uses a two-beam laser altimetry system in which each beam is equipped with a laser footprint camera (LFC) to provide geometric processing of the laser footprint images that assist in optical image stereo mapping. Because of the violent vibrations during launch and the difference in the environment before and after entering orbit, the key parameters for geometric processing of the laser footprint images may change, which will cause large geolocation errors. Therefore, it is essential to carry out on-orbit calibration and validation for the laser footprint cameras. This study first constructs a rigorous geometric positioning model for the LFC of the GF-7 satellite and analyses various error sources that affect the geometric positioning accuracy of laser footprint images. Then, a comprehensive calibration method, which effectively eliminates the distortion of the LFC optical system, and the positioning error caused by the long-period jitter of the satellite platform, is proposed based on the multi-scene images combined with image simulation. The proposed method can effectively eliminate various errors that affect the geometric positioning accuracy of the GF-7 laser footprint image. The internal geometric positioning accuracy of the calibrated LFC is better than 0.7 pixels, and the absolute geometric positioning accuracy is within 6.0 m after using precise post-processing orbital and attitude data. Our study will contribute to the processing and application of laser altimetry data from the GF-7 satellite.
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