At present, aerospace development puts forward an urgent need for the integrative system design in satellite with MWIR/LWIR hyperspectral imaging spectrometer to provide the solution of target detection problem under the circumstance of weak thermal contrast between target and background at night, which can hardly be solved by traditional thermal infrared imaging system. In order to efficiently optimize the imaging index of the MWIR/LWIR hyperspectral imaging spectrometer, i.e. ground sample distance (GSD), spectral resolution, noise equivalent temperature difference (NETD), this paper proposed a novel optimized integrative system design method based on evaluation for target detection performance through multidimensional signal-to-clutter ratio (SCR). For assumed Gaussian target and background statistics, multidimensional SCR is the primary parameter describing the detection performance of a variety of detection algorithms based on the generalized maximum likelihood formulation, especially when the thermal contrast between target and background approach to zero. Therefore, we calculate the multidimensional SCR from MWIR/ LWIR hyperspectral images that are obtained through the simulation of satellite borne hyperspectral imaging chain with imaging indices, as the equivalent of detection performance. Based on the training datasets composed of multidimensional SCR and imaging indices, we can use random forest regression to identify the sensitivities of different imaging indices to multidimensional SCR. The sensitivity analysis of multidimensional SCR can help to determine the key to index optimization, guiding the integrative system design. More importantly, the relationship between the SCR and imaging indices can be predicted through random forest learning, which can be applied to the further global optimization of imaging indices with related optimization algorithms. With our proposed method, the integrative system design is closely associated to the demand for target detection task, meeting the satellite-borne detection performance requirements, and the manufacturing cost could be reduced due to the absence of excessive index optimization.
Coded aperture imaging spectrometer is a new type of hyperspectral imaging instrument. The space-borne hyperspectral imager makes images by pushing and sweeping. In the ideal imaging model, it is assumed that one pixel is separated between two adjacent frames so that the target information can be accurately reconstructed. When coding aperture imaging is performed under motion compensation, the moving distance of the object image on the focal plane at each imaging time is different, and there is an amount of dislocation, resulting in decoding error of the decoded and restored data along the direction of the orbit, and the phenomenon of ground object "double shadow" and spectral decoding distortion appear in the simulation image. The amount of misalignment under different compensation modes is different, resulting in different decoding errors. The mathematical model of target data encoding and decoding in push-sweep coded aperture imaging and the mathematical model of field of view optical axis angular velocity in motion compensation mode were constructed. The simulation method of coded aperture imaging hyperspectral data under motion compensation was established, and the simulation data quality was analyzed. Through data quality analysis, it can be seen that under the uniform angular velocity mode, the uniform ground velocity mode and the uniform integral time mode, the cumulative amount of dislocation decreases successively, which is 5.7 m, 0.7 m and 0 m, respectively. The "double shadow" phenomenon of the simulated image becomes less and less obvious, and the image quality becomes clearer and clearer. Meanwhile, the restoration and reconstruction accuracy of the coding aperture imaging improves successively.
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