Deep learning reconstruction enables full-Stokes single compression in polarized hyperspectral imaging

Polarized hyperspectral imaging,which has been widely studied worldwide,can obtain four-dimensional data including polarization,spectral,and spatial domains.To simplify data acquisition,compressive sensing theory is utilized in each domain.The polarization information represented by the four Stokes parameters currently requires at least two compressions.This work achieves full-Stokes single compression by introducing deep learning reconstruction.The four Stokes parameters are modulated by a quarter-wave plate(QWP)and a liquid crystal tunable filter(LCTF)and then compressed into a single light intensity detected by a complementary metal oxide semiconductor(CMOS).Data processing involves model training and polarization reconstruction.The reconstruction model is trained by feeding the known Stokes parameters and their single compressions into a deep learning framework.Unknown Stokes parameters can be reconstructed from a single compression using the trained model.Benefiting from the acquisition simplicity and reconstruction efficiency,this work well facilitates the development and application of polarized hyperspectral imaging.

基于空-谱编码的压缩感知高光谱计算成像

压缩感知高光谱计算成像技术是当前高光谱计算成像领域的研究热点之一,其能够在保持系统元器件物理特性不变的前提下,有效地提升成像质量。本文概述了高光谱计算成像的研究背景和基本概念,详细介绍了压缩感知高光谱计算成像系统的发展现状,重点阐述了本团队提出的基于空-谱编码的压缩感知高光谱计算成像技术,并对其系统组成、数理模型以及最新进展进行了说明。通过总结压缩感知高光谱计算成像的背景知识以及空-谱编码压缩感知高光谱计算成像的研究工作,力求为科研人员探索压缩感知高光谱计算成像新体制带来新的思路,促进高光谱计算成像技术的发展。

FY-3A MERSI短波红外通道图像增益突跳自动识别和归一

搭载于我国第二代极轨气象卫星风云三号A星(FY-3A)上的中分辨率光谱成像仪(MERSI),肩负着获取全球图像和辐射资料的作用。然而,太空环境影响等因素,导致短波红外通道发生在轨增益突跳现象,大大限制了数据的定量应用。通过对冷空、星上定标器和黑体等MERSI星上观测目标的分析,选择冷空作为增益突跳识别的指征目标。提出自动识别、增益归档和档位归一化方法,采用日识别和天内精检测的方法获取增益突跳时空信息,采用分类统计突跳数据的方法获取突跳的在轨增益档位,并利用星上定标源目标和对地观测图像进行归一化效果验证,实现了对1.64μm和2.13μm两通道整个生命期91次和18次突跳事件的精确定位,获得了8个在轨增益档位和全部突跳事件的档位信息,并得到了理想的归一化效果验证。研究结果同样显示,增益突跳事件大多发生在对地目标扫描过程,而且每个探元增益突跳的时间不尽相同。由于地球目标的复杂性,基于物理方法很难实现对突跳帧图像的归一。该研究成果不仅有助于FY-3A MERSI图像质量的提升,而且对历史数据再处理也有重要支撑作用。