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根据前文原理构建四维云层空间分布模型,代入风向角、云层移动方向参数,开展仿真分析。风向角和云层移动方向示意图如图2所示。
对地球静止轨道凝视成像系统进行成像仿真,参数设置如表1所示。
Parameter Value Parameter Value Imaging time/min 1 Orbital altitude/km 36000 Frame rate/Hz 1 Earth radius/km 6378 Image size/pixel 30000×30000 NESR/W·sr−1·m−2 2×10−5 Focal length/mm 2200 Jitter amplitude/mm 5×10−2 Cell size/mm 1×10−2 Average defocus amount/mm 1×10−3 Integration time/s 5×10−4 Other MTF 0.8 Aperture diameter/mm 500 Wavelength/μm 3-5 Wind angle/(°) 0, 45 Overall cloud speed/pixel·s−1 0, 10 Table 1. Detection system simulation parameter settings
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仿真实验结果示例如图3(a)~(c)所示,其中,图3(a)风向为0°,云层的移动速度为0 pixel/s;图3(b)风向为0°朝向图片右方,云层的移动速度为10 pixel/s;图3(c)风向为45°朝向图片右上角,云层的移动速度为10 pixel/s。
由图3(a)可知,当场景中无风时,随着时间的推移,云层的运动只有内部形状结构的变化,不存在整体的移动;由图3(b)、(c)可知,当场景中有风时,随着时间的推移,云层在整体随着风向移动,且移动速度与风速有关,同时云层内部形状结构也在发生变化,连续帧图像中的动态云变化平滑、过渡自然,验证了文中动态云仿真方案的可行性和有效性。
比较改进方法与传统方法(基于多尺度叠加的动态云场景图像仿真)的计算效率,实验结果如表2所示。
Items Traditional method/s Improved method/s Size/pixel 10000×10000 4188.84 298.56 50000×50000 125655.25 8196.47 100000×100000 629365.63 38113.60 Type Cirrus 109915.45 6581.97 Cirrocumulus 3891.56 360.33 Stratus 65782.28 4635.82 Coverage 30% 15529.43 1125.32 50% 62117.64 3981.90 70% 123438.89 7261.11 Table 2. Results of comparing computational efficiency of improved method and traditional method
通过对连续500帧不同尺寸的大尺度动态云图像仿真对比实验可知,时序平滑多尺度叠加方法在计算效率上明显优于传统方法:针对覆盖率70%、卷云类型的图像,随着图像尺寸的增大,该方法在计算效率提升了14.03倍、15.33倍和16.51倍;针对卷云类型、云覆盖率分别为30%、50%和70%的图像,相对传统方法的计算效率分别提升了13.80倍、15.60倍和17.01倍;针对云覆盖率70%、云类型分别为卷云、卷积云和层云的图像,相对于传统方法,计算效率分别提升了16.69倍、10.81倍和14.18倍。基于时序平滑多尺度叠加方法的动态红外云场景仿真方法在计算效率方面的优势更为显著。
Dynamic infrared cloud scene simulation based on time series smoothing multiscale superposition
doi: 10.3788/IRLA20220656
- Received Date: 2021-09-09
- Rev Recd Date: 2022-05-10
- Available Online: 2022-08-31
- Publish Date: 2022-08-31
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Key words:
- image simulation /
- infrared system /
- dynamic cloud scene /
- multiscale superposition
Abstract: The cloud scene in the space-based infrared observation scene has the characteristics of geometric structure dynamic change, scale dynamic change, radiation dynamic change and is coupled with the space-based dynamic detection link, which will have a great impact on the detection efficiency of the system. Therefore, it is very important to carry out research on cloud scene simulation methods for the design of space-based infrared optical satellite systems. This paper proposes a dynamic cloud image simulation method based on the time series smoothing multiscale superposition method to solve the problems of low computational efficiency and large memory usage of traditional simulation methods in large-scale dynamic cloud image simulation applications. The interframe interpolation method is used to realize the change in the shape and structure of the dynamic cloud layer, which improves the computational efficiency by more than 10 times. Realistic simulation of the overall structural change in the position and shape of the clouds realizes the simulation of large-scale dynamic cloud images.