ShuangQing-1 (Luojia3-01) Multimode Imaging Sample Dataset
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摘要: 针对当前多数高分辨遥感卫星面向用户服务存在获取数据种类单一问题，该文公开了双清一号(珞珈三号01星)多模式成像样例数据集，涵盖了面阵推扫、面阵推帧和视频凝视等多种成像模式，包含城市、水体、山区、机场等不同目标区域的典型数据样本。该数据集由信号数据解码、Bayer插值、相对辐射校正、几何定位、视频稳像和3维重建等步骤处理构建；同时，对在轨定标、兴趣区产品快速生产、高清视频几何稳像和多角度3维重建等关键算法做了深入探讨和研究。最后，对样本数据集从图像标准产品、凝视视频产品和实景3维产品等3个方面进行了可视化展示和定量化精度评价。Abstract: Herein, the Shuangqing-1(Luojia3-01) multimode imaging sample dataset is presented to address the problem of limited data types provided for user services by remote sensing satellites with the highest resolution. This dataset includes various imaging modes, such as push-scan, array push-frame, and video staring; hence, it covers typical data samples from different target areas ,such as urban regions, water bodies, mountainous regions, and airports. The construction of this dataset involves signal data decoding, Bayer interpolation, relative radiometric correction, geometric positioning, video stabilization, and three-dimensional reconstruction. Additionally, in-depth discussions and investigations are conducted on key algorithms, such as on-orbit calibration, rapid production of area of interest products, high-definition video geometric stabilization, and multi-angle three-dimensional reconstruction. Finally, the sample dataset is visually displayed and quantitatively evaluated from three aspects: image standard, video staring, and real-world three-dimensional products.
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表 1 双清一号数据技术指标
参数 指标数值 成像模式 视频凝视/面阵推帧/面阵推扫 空间分辨率 0.7 m@500 km 地面幅宽 双CMOS优于10 km 视频帧频 2～12 Hz 图像格式 Bayer彩色图像 量化位数 8 bit/12 bit 几何定位精度 优于30 m 相对辐射精度 优于3% 
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表 2 传感器校正算法
输入：分片影像、成像时间、姿轨数据、地球自转参数、高程
dem等输出：有理函数模型RFM和虚拟大影像 1 分别构建单片原始影像和待校正虚拟影像的严格几何成像模
型：
$\left[ \begin{gathered} X \\ Y \\ Z \\ \end{gathered} \right] = \dfrac{1}{\lambda } \cdot R_{ {\rm{J2000} } }^{ {\rm{WGS84} } }(t)R_{ {\rm{body} } }^{ {\rm{J2000} } }(t)R_{ {\rm{senor} } }^{ {\rm{body} } }\left[ \begin{gathered} x + {x_0} + \Delta x \\ y + {y_0} + \Delta y \\ f \\ \end{gathered} \right] + \left[ \begin{gathered} {X_{ {\rm{GPS} } } }(t) \\ {Y_{ {\rm{GPS} } } }(t) \\ {Z_{ {\rm{GPS} } } }(t) \\ \end{gathered} \right]$2 建立分片原始影像与待校正虚拟影像的坐标映射关系； ${(s,l)}_{ {\rm{ori} } }\iff {(x,y)}_{vir},其中{(s,l)}_{ {\rm{ori} } }\Rightarrow (B,L,H)\Rightarrow {(x,y)}_{ {\rm{dst} } }$ 3 影像重采样(CUDA并行) 3.1 对单片原始影像进行格网划分，每个格网(grid)包含
$(w \times h)$个像素3.2 设定CUDA线程参数，包括： (a) 每个CUDA格网(grid)中的块(block)数目：$({B_x},{B_y})$ ; (b) 每个CUDA块(block)中的线程(thread)数目：$({T_x},{Y_y})$; (c) 每个CUDA线程(thread)中处理的像素数目:$({P_x},{P_y})$ 确保$({B_x} \times {T_x} \times {P_x},{B_y} \times {T_y} \times {P_y}) \ge (w \times h)$ 3.3 每个格网(grid)中的$ ({B_x} \times {T_x},{B_y} \times {T_y}) $个线程(thread)
并行执行：每个线程处理$({P_x},{P_y})$个像素 每个像素 (a) 计算虚拟影像像点$ {(x,y)_{{\rm{vir}}}} $所对应单片原始影像像
点坐标${(s,l)_{{\rm{ori}}}}$;(b) 分别计算像点${(s,l)_{{\rm{ori}}} }$邻域像素的灰度值； (c) 利用多项式重采样方法，计算出${(s,l)_{{\rm{ori}}} }$灰度值. 像素循环结束 影像格网循环结束
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