珞珈三号01星是全球首颗互联网智能遥感科学实验卫星，由武汉大学联合航天东方红卫星有限公司等单位联合研制，用于验证遥感数据实时获取、智能处理、压缩传输等关键科学问题，探索天基空间网络遥感信息服务新模式。本期精选《武汉大学学报（信息科学版）》珞珈三号01星专栏的6篇双语文章，希望能为相关领域学者提供借鉴与参考，欢迎阅读！
01
一种面向卫星在轨自主任务规划的快速精准轨道预报方法
A Fast and Accurate Orbit Prediction Method for Satellite On-Orbit Autonomous Mission Planning
【摘要】卫星在轨自主任务规划需要高精度的轨道预报数据，计算待观测目标的可成像时间窗口、成像姿态需求，结合其他约束条件来完成任务规划过程。针对卫星星上计算资源有限的情况，为了减少轨道预报过程的资源消耗、满足自主任务规划的需求，提出一种在长周期的轨道预报中预报精度高、资源消耗少的轨道预报方法。基于两行根数（two-line element，TLE）及简化普适摄动模型，以多条轨道数据作为输入，迭代计算生成TLE，完成轨道预报，并计算轨道预报误差和模拟观测目标的成像参数初值误差，进行定量评价。实验结果表明，所提方法仅需少量输入数据即可完成72 h的轨道预报，预报精度优于4 km，平均耗时12.76 s，在长周期的轨道预报中预报精度高、运行速度快；使用轨道预报数据计算模拟观测目标的开始成像时间误差优于0.2 s，三轴方向上的成像姿态初值误差均优于0.03°，小于智能遥感卫星珞珈三号01星相机成像过程姿态指向精度指标。所提方法预报精度高，需求的时空资源少，对于星上在轨自主任务规划具有重要意义。
【Abstract】Objectives: Satellite on-orbit autonomous mission planning requires high-precision orbit prediction data to calculate the feasible imaging time windows and imaging posture requirements for the intended observation targets. This process involves integrating these factors with other constraint conditions to accomplish the mission planning. Methods: In the context of limited computational resources onboard satellites, this paper proposes a novel method to reduce resource consumption in orbit prediction process and meet the demands of autonomous mission planning. First, based on two-line element (TLE) and a simplified general perturbation model, the proposed method utilizes multiple sets of orbital data as input and introduces a new measurement metric. Then, it iteratively generates TLE by rationally setting data weights and progressively adjusting them to get the results of orbit prediction. Finally, the total error for orbit prediction and the initial value error of imaging parameters for simulating observation targets are calculated for quantitative evaluation. Results: Experimental results demonstrate that the proposed method requires only a small amount of input data to achieve a 72-hour orbit prediction with the accuracy better than 4 km and the average computation time of 12.76 s. Compared to high precision orbit propagator model, the proposed method provides higher prediction accuracy and faster execution speed in long-period orbit prediction. The calculated start imaging time error for simulated observation targets is less than 0.2 s, and the initial imaging posture errors in the three-axis directions are all better than 0.03°, which is lower than the attitude pointing accuracy requirement for the imaging process of camera onboard Luojia3-01 satellite. Conclusions: The proposed method offers high prediction accuracy and demands fewer temporal and spatial resources, making it of great significance for satellite on-orbit autonomous mission planning.
02
融合色差梯度和颜色相关性的Bayer成像插值方法
Bayer Interpolation Method by Integrating Color-Difference Gradient and Color Correlation
【摘要】为减轻卫星影像数据星地传输压力，Bayer成像模式被越来越广泛地应用于遥感卫星的成像系统，由于Bayer影像中每个像素只存储一个颜色通道的信息，存在信息缺失，因此必须经过Bayer成像插值重建为全彩色影像才能用于后续应用。现有多数Bayer成像插值方法存在地物边缘处细节缺失和色调偏移问题。针对上述问题，提出了一种融合色差梯度和颜色相关性的Bayer成像插值方法。首先提取原始Bayer影像的各通道间色差梯度信息，使用归一化的色差梯度函数赋予各方向色差估计值的相应权重，使插值沿着边缘方向进行，以保护地物边缘处细节信息，然后利用不同颜色通道间的相关性设置色比约束，调整局部滑动窗口内3个通道的相对比例，以改善插值结果中的色调偏移现象，使重建的全彩色影像色调真实、细节完整。通过在模拟影像和珞珈三号01星获取的真实Bayer影像上的实验对比，所提方法与对比方法相比，重建影像在地物边缘处的伪色问题有明显改善，具有最高的峰值信噪比、结构相似性指数和最低的均方误差、自然图像质量评价分数，且与参考影像相比没有空间分辨率损失。所提方法已成功应用于珞珈三号01星数据处理系统中，进行在轨和地面的Bayer成像插值处理，为珞珈三号01星数据的各项应用提供了有效保障。
【Abstract】Objectives: In order to alleviate the pressure of satellite image data transmission between satellite and ground, the Bayer imaging mode is increasingly being used in remote sensing satellite imaging systems. The information loss occurs because each pixel in the Bayer image only stores information from one color channel. Therefore, a full-color image must be reconstructed by Bayer interpolation for subsequent applications. However, the existing interpolation methods suffer from false colors at image details and overall color tone shift issues. Methods: In response to the above issues, this article proposes a Bayer interpolation method by integrating color-difference gradient and color correlation. First, the proposed method extracts the color difference gradient information between each channel of the original Bayer image, and uses a normalized color difference gradient function to assign corresponding weights to the estimated color difference values in each direction. The interpolation is carried out along the edge direction to protect the detail information at the edge. Then, the color ratio constraints are set according to the correlation between different color channels, and the relative proportion of the three channels in the local sliding window is adjusted to improve the color shift phenomenon in the interpolation results, which makes the reconstructed full-color image have real color tones and complete details. Results: The experimental comparison is conducted on simulated images and real Bayer images obtained from Luojia3-01 satellite, and the proposed method has significantly improved the false color problem at the edges of ground objects. Compared the comparison methods, the proposed method has the highest peak signal-to-noise ratio and structural similarity index, and the lowest mean square error and natural image quality evaluation score. Conclusions: The proposed method can effectively improve the problem of missing details at the edges of ground features and enhance the quality of reconstruction. It has been successfully applied to the data processing system of Luojia3-01 satellite to interpolate Bayer images in orbit and on the ground, providing effective guarantees for the various applications of Luojia3-01 satellite data.
03
基于物方一致性的珞珈三号01星视频数据在轨实时稳像
Object-Space-Consistency-Based Real-Time Stabilization Approach for Luojia3-01 Video Data
【摘要】利用高分辨率敏捷光学卫星的凝视观测能力，对特定区域或目标进行连续观测，从而实现区域监测、目标跟踪等应用，是高分辨率光学卫星的应用热点之一。获取的凝视观测数据经过几何与稳像处理后，能得到稳定的高质量视频数据。但受制于过程中需要的大量运算，星载设备计算与存储能力难以满足，该处理只能由地面系统在事后完成，信息获取的时效性低。针对该问题，基于珞珈三号01星数据特点与星载硬件计算能力，提出一种基于物方一致性的在轨视频帧实时稳像方法。所提方法利用实时几何定位，在卫星成像过程中实时提取并对目标区域影像进行几何校正；同时利用影像帧之间地理信息的一致性，对相邻影像帧进行实时配准，保证帧间相对精度；在此基础上，通过构建算法并行流水线，实时生成带有地理编码的兴趣区视频帧序列。使用珞珈三号01星真实数据与帧率（6帧/s）进行实验，结果表明，基于珞珈三号01星嵌入式星载处理硬件，所提方法能够实现快于6帧/s的处理速度，同时获得的视频帧序列带有地理编码，且帧间稳像精度达0.328像素，可满足星载实时服务需求。
【Abstract】Objectives: High-resolution agile optical satellites can continuously observe specific regions or targets, thereby realizing applications such as regional monitoring and target tracking. In order to obtain stable and high-quality video data, continuous observation data need to be geographically corrected and stabilized. This process requires a large amount of calculation, but the on-board computing and storage device cannot meet the demand. Therefore, this process can only be completed afterwards by on-ground system, resulting in a significant delay in information acquisition. Methods: This paper proposes an object-spaceconsistency-based real-time stabilization approach for Luojia3-01 satellite video data, which is based on data characteristics and computing capability of embedded on-board hardware. The proposed approach employs real-time geometric positioning to extract and correct the region of interest (ROI) in real-time during satellite imaging. Additionally, it employs the consistency of geographical information in real-time align adjacent image frames to ensure the relative accuracy between sequence frames. On the basis, it generates a sequence of video frames of ROI with geographical encoding in real-time through the construction of a parallel pipeline of algorithms. Results: The experiments are conducted by the actual data of Luojia3-01 satellite with the frame rate of 6 frame per second, and the proposed approach can realize the faster processing based on the embedded on-board processing hardware. Furthermore, the obtained video frame sequence with geographic encoding and the inter-frame stabilized accuracy is better than 0.328 pixels. Conclusions: The proposed approach can meet the requirements of on-board real-time service.
04
珞珈三号01星视频智能插帧应用研究
Application of Intelligent Video Frame Interpolation for Luojia3-01 Satellite
【摘要】光学视频卫星获取的高动态、高空间分辨率的数据为对地动态观测提供了新的技术手段。2023年初发射的珞珈三号01星是新一代智能测绘遥感科学试验卫星，该星可通过凝视成像模式获取对地高清彩色视频，但原始视频帧率较低，仅为6帧/s。为进一步提升珞珈三号01星视频的流畅度，降低视觉观感的卡顿效果，开展了面向珞珈三号01星视频插帧的相关研究。首先，针对卫星凝视成像过程中的误差进行了分析，提出了一种基于帧间透视变换模型的视频稳像方法，实现了原始视频数据的预处理；然后，考虑到日常可见光视频与卫星视频之间存在较大差异，且目前暂无可用的卫星视频插帧数据集，基于预处理后的稳像视频构建了一个涵盖不同场景的卫星插帧数据集Luojia3＿VFISet；最后，基于无需光流模块参与的FLAVR（flow-agnostic video representation）视频插帧网络，通过将不同尺度的特征编码信息引入解码过程，提出了FLAVR＿Plus视频插帧网络，进一步提升了卫星视频的插帧效果。实验结果表明，FLAVR＿Plus插帧结果的测量峰值信噪比达到35.544 6 dB，精度提升约0.5%～7.2%，结构相似性可达0.917 9，同比提升约0.5%～8.7%。所构建的Luojia3＿VFISet数据集有助于相关研究工作的开展，提出的FLAVR＿Plus视频插帧网络针对不同场景均能生成质量良好、无明显拖影的中间帧，可有效提升珞珈三号01星视频的流畅度，为后续的卫星视频相关应用提供更多的帧间信息。
【Abstract】Objectives: The dynamic and high spatial resolution data obtained by optical video satellites provide a new technical means for the dynamic observation of the earth. As a new generation of intelligent mapping remote sensing scientific test satellite, Luojia3-01 satellite was launched in early 2023, and it could acquire high-definition color video by gazing mode. However, the original video frame rate is low, only 6 frame per-second. To further improve the fluency and reduce the stuttering effect of visual perception, this paper carries out related research on intelligent video frame interpolation (VFI) of Luojia3-01 satellite video. Methods: First, a satellite video image stabilization method based on the perspective transformation model is proposed after analyzing the imaging errors during the gaze mode. Second, satellite video is greatly different from daily video, and there is no available satellite VFI dataset at present. This paper builds a VFI dataset termed as Luojia3_VFISet based on the stabilized satellite videos, which covers different scenes. Finally, based on the flow-agnostic video representation (FLAVR) VFI network without optical flow module, the FLAVR_Plus VFI network is proposed to further improve the interpolation effect for satellite video by introducing the feature coding information of different scales from the encoder into the decoding process. Results: The experimental results show that the interpolation of FLAVR_Plus VFI network raises the peak signal to noise ratio (PSNR) and structural similarity (SSIM) to 35.544 6 dB and 0.917 9, respectively, and it enhances the quality of synthesized intermediate frames with hardly-observed artifacts under different scenarios. Compared with other methods, the proposed network can improve the PSNR by 0.5% to 7.2% and the SSIM by 0.5% to 8.7%, respectively. Conclusions: In this paper, the application of satellite VFI is studied by taking Luojia3-01 satellite as an example. The proposed Luojia3_VFISet dataset is conducive to the development of related research. The proposed FLAVR_Plus VFI network can effectively improve the fluency of the Loujia3-01 satellite video by generating interframes without artifacts and provide more interframe information for subsequent applications.
05
珞珈三号01星冰雪/云场景影像偏色校正处理
Color Correction of Luojia3-01 Satellite Images with Partial Snow or Cloud Cover
【摘要】珞珈三号01星采用Bayer成像模式，其获取的绿色通道信号相对较强，但由于相机通常采用固定的曝光与增益设置，导致珞珈三号01星拍摄的含冰雪/云覆盖影像会出现严重偏绿现象。针对该问题，提出了一种结合颜色通道补偿与直方图间互相关关系的直方图重叠白平衡方法。为提升影像的细节信息，通过低灰度值区域的分布比例构建伽马函数，实现拉伸处理；为提升各颜色通道分布的相似度，使用影像的标准差信息计算权重，补偿衰减通道，并基于YCbCr颜色空间保持亮度信息，避免影像背景亮度变化；为改善偏色情况并保持色度均值的重合，通过互相关关系进行色度直方图重叠处理，根据冰雪/云场景影像的特征自适应调整参数。对校正后的结果影像进行主观分析与客观评价，并与多种主流的颜色校正方法进行对比验证。实验结果表明，所提方法颜色校正效果更好、影像地物颜色更自然，且能更好地保留地物信息。
【Abstract】Objectives: The green channel received by Luojia3-01 satellite in Bayer imaging mode has relatively strong signals. The camera often adopts a fixed exposure and gain setting for images containing high saturation pixel values, which comprehensively leads to the color deviation of the images with partial snow or cloud cover captured by Luojia3-01 satellite. Methods: To solve the above problems, a histogram overlapping white balance method combining color compensation and cross-correlation is proposed. First, in order to further improve the details of the image, the Gamma function is constructed through the distribution proportion of the region of low gray value to enable adaptive gamma correction processing. In order to improve the similarity of the distribution of color channels, the standard deviation of the image is used to calculate the weight to compensate for the attenuation channels, and the brightness information is maintained based on YCbCr color space to avoid the background brightness change of the image. Then, in order to improve the color deviation and maintain the overlap of the color mean, the color histogram is overlapped by using the cross-correlation relationship, and the parameters are adjusted according to the features of the images with partial snow or cloud cover. Finally, subjective analysis and objective evaluation of the corrected images are carried out. Results: The proposed method has the best processing effect in retaining the information of other ground objects except snow or cloud, and the information entropy value is the largest. The calculated value of the color correlation index obtained by processing the scene images with different amounts of snow and ice or cloud coverage also reaches the optimal value, and the color correlation is the highest compared with other methods. Other color correction methods, such as gray-world algorithm and gray-edge algorithm, are not effective, and it is difficult for other methods to achieve a balance between improving the color cast and preserving image information. Conclusions: The proposed method has better color correction effect, more natural color, and can better preserve feature information.
06
双边加权组稀疏残差约束的面阵卫星影像去噪
Area-Array Satellite Images Denoising Based on Bilateral Weighted Group Sparsity Residual Constraint Model
【摘要】传统的组稀疏表示模型受到噪声的影响可能无法准确估计每个影像组的稀疏性，从而导致对理想影像的复原失真。提出了双边加权的组稀疏残差约束模型，引入组稀疏残差约束，首先利用稀疏编码系数的非局部自相似性获得理想影像的组稀疏系数估计，然后约束对应退化影像的组稀疏系数来逼近这一估计。由于面阵卫星影像噪声较为复杂，用简单加性高斯白噪声难以精确建模，将两个权重矩阵分别引入组稀疏残差约束的数据保真项和正则化项中，以表征影像和噪声的统计特性。使用模拟数据和珞珈三号01星获取的真实影像进行实验，在模拟实验中，双边加权组稀疏残差约束模型在去除加性高斯白噪声和空间异质噪声方面表现优于其他对比方法。在真实影像实验中，使用该模型去噪后的影像熵值相较于三维块匹配滤波方法、多波段加权核范数最小化方法、非局部中心化稀疏表示方法、低秩化组稀疏表示方法和三边加权稀疏编码方法，分别提升了2.03%、1.18%、1.26%、1.24%和2.10%。结果表明，双边加权组稀疏残差约束模型在保留影像边缘细节和消除真实影像噪声方面优于对比方法。
【Abstract】Objectives: The conventional group sparse representation (GSR) model encounters challenges in precisely estimating the sparsity of individual image groups, because the influence of noise results in distortion in the restoration of original image group. Methods: This paper introduces a strategy denominated as the group sparse residual constraint. This strategy leverages non-local self-similarity priors to derive precise estimate of sparse coefficients for each original image group. Subsequently, the sparse coefficients of corresponding degraded image groups are constrained to approximate this estimate. Furthermore, the intricate nature of noise in area-array satellite images (e.g., Luojia3-01 satellite images), surpasses the simplicity of additive white Gaussian noise (AWGN). Consequently, conventional GSR model tailored for AWGN exhibits a reduction in efficacy when deployed in real image denoising scenarios. To address this, this paper introduces two distinct weight matrices into the data fidelity term and regularization, respectively, with the intent of characterizing the statistical features inherent in both images and noise. This paper adopts the alternating direction method of multipliers for optimizing the proposed image denoising task. Results: The effectiveness of the proposed model is validated by the experiments using simulated data and real images obtained from the Luojia3-01 satellite. In the experiments of simulated data, the proposed model outperforms other methods in removing AWGN and spatially variant noise. In the experiments of real images, the entropy of denoised images by the proposed model shows improvements of 2.03%, 1.18%, 1.26%, 1.24%, and 2.10%, compared to the block-matching and 3-D filtering method, multi-channel weighted nuclear norm minimization method, nonlocally centralized sparse representation method, low-rankness guided group sparse representation method, and trilateral weighted sparse coding method, respectively. Conclusions: The proposed method exhibits good performance in processing area-array satellite images with low signal-to-noise ratios and complex noise structures, significantly enhancing the quality of the image. By effectively reducing noise while preserving crucial details and edges, it provides a reliable solution for enhancing the usability and interpretability of area-array satellite images, particularly in challenging conditions.
期刊推荐
《武汉大学学报（信息科学版）》是教育部主管、武汉大学主办、国内外公开发行的测绘专业学术期刊，创刊于1957年，其前身是《武汉测绘科技大学学报》。现任主编为中国工程院院士李建成。
办刊宗旨：期刊依托中国测绘学科优势，立足国内、面向国际，开放办刊，致力于打造中国优秀的测绘期刊品牌。通过发表具有创新性和重大研究价值的测绘学术成果，展示中国测绘研究的最高水平，促进测绘学术交流，引导测绘研究方向，推动测绘科技进步，服务测绘行业发展。
刊登内容：数字摄影测量、遥感技术与应用、地图学与地理信息系统、卫星大地测量、物理大地测量与地球动力学、测绘工程、图形图像学等学科及相关学科的科研成果。稿件要求具有较高的学术水平或重大应用价值，具有创新性、完整性。
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