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An algorithm to correct the degradation of interference spectral imaging caused by motion errors

机译:一种校正运动误差引起的干涉光谱成像退化的算法

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In the spectral imaging process of interference imaging spectrometer, the impact caused by motion errors of satellite platform is not only the degradation of spatial resolution but also the aliasing and distortion of spectral information. The degradation function is modeled though analyzing the degradation mechanism of spectral imaging. And a correction algorithm based on interference sequences is proposed to solve this problem. In this algorithm, the interference sequences is regarded as an integer and the degradation model is thought to be three-dimensional, including two spatial dimensions and one spectral dimension. And the degraded interference sequences are the convolution of the original sequences and the degradation function. According to the vibration data measured by POS on the satellite, the degradation function can be obtained. Then the corrected interference sequences can be got by de-convolution of the degraded sequences and the degradation function though Hopfield neural network based on continuous variation of states. And by reconstructing of the corrected sequences, the corrected images and spectrums can be got finally. The simulated results show that this algorithm can correct the degradation of images and the distortion of spectrums caused by motion errors of satellite's attitudes effectively.
机译:在干涉成像光谱仪的光谱成像过程中,卫星平台运动误差造成的影响不仅是空间分辨率的下降,而且是光谱信息的混叠和失真。通过分析光谱成像的退化机理对退化函数进行建模。提出了一种基于干扰序列的校正算法。在该算法中,干扰序列被认为是整数,并且退化模型被认为是三维的,包括两个空间维度和一个频谱维度。退化的干扰序列是原始序列和退化函数的卷积。根据POS在卫星上测得的振动数据,可以获得衰减函数。然后,通过基于状态连续变化的Hopfield神经网络,通过对退化序列和退化函数进行反卷积,可以得到校正后的干扰序列。通过重构校正后的序列,可以最终得到校正后的图像和光谱。仿真结果表明,该算法可以有效地纠正由于卫星姿态运动误差引起的图像退化和频谱畸变。

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