Prof. Jing Cao Profile

Prof. Jing Cao

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Publications (2)

Proceedings Article | 23 October 2023 Presentation + Paper

Optimized integrative system design in satellite with MWIR/LWIR hyperspectral imaging based on multidimensional SCR and random forest learning

Shaocong Liu, Zhen Li, Jing Cao, Tinghao Liu, Xianfei Qiu, Haixiao Yin

Proceedings Volume 12736, 127360K (2023) https://doi.org/10.1117/12.2684374

KEYWORDS: Satellites, Hyperspectral imaging, Satellite imaging, Imaging systems, Design and modelling, Target detection, Random forests, Infrared radiation, System integration, Infrared imaging

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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.

Proceedings Article | 8 March 2018 Paper

A parallel method of atmospheric correction for multispectral high spatial resolution remote sensing images

Shaoshuai Zhao, Chen Ni, Jing Cao, Zhengqiang Li, Xingfeng Chen, Yan Ma, Leiku Yang, Weizhen Hou, Lili Qie, Bangyu Ge, Li Liu, Jin Xing

Proceedings Volume 10611, 1061109 (2018) https://doi.org/10.1117/12.2283380

KEYWORDS: Atmospheric corrections, Remote sensing, Multispectral imaging, Spatial resolution, Image processing, Image segmentation, Satellites

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