Atmospheric correction is an essential step in hyperspectral imaging and target detection from spectrometer remote sensing data. State-of-the-art atmospheric correction approaches either require extensive filed experiments or prior knowledge of atmospheric characteristics to improve the predicted accuracy, which are computational expensive and unsuitable for real time application. To take full advantages of remote sensing observation in quickly and reliably acquiring data for a large area, an automatic and efficient processing tool is required for atmospheric correction. In this paper, we propose a time-dependent neural network for automatic atmospheric correction and target detection using multi-scan hyperspectral data under different elevation angles. In addition to the total radiance, the collection day and time are also incorporated to improve the time-dependency of the network and represent the seasonal and diurnal characteristics of atmosphere and solar radiation. Results show that the proposed network has the capacity to accurately provide atmospheric characteristics and estimate precise reflectivity spectra with $95.72\%$ averaged accuracy for different materials, including vegetation, sea ice, and ocean. Additional experiments are designed to investigate the network’s temporal dependency and performance on missing data. The error analysis confirms that our proposed network is capable of estimating atmospheric characteristics under both seasonally and diurnally varying environments and handling the influence of missing data. Both the predicted results and error analysis are promising and demonstrate that our network has the ability of providing accurate atmospheric correction and target detection in real time.

大气校正是高光谱成像和从光谱仪遥感数据进行目标检测的必不可少的步骤。最新的大气校正方法要么需要进行广泛的实验，要么需要大气特征的先验知识以提高预测的准确性，这在计算上是昂贵的并且不适用于实时应用。为了充分利用遥感观测的优势，可以快速，可靠地获取大面积的数据，需要一种自动高效的处理工具来进行大气校正。在本文中，我们提出了一种基于时间的神经网络，用于在不同仰角下使用多扫描高光谱数据进行自动大气校正和目标检测。除总辐射外，收集日还结合了时间和时间以改善网络的时间依赖性，并代表了大气和太阳辐射的季节性和昼夜特性。结果表明，所提出的网络能够准确地提供大气特征并估计精确的反射率光谱，$95.72％$不同材料（包括植被，海冰和海洋）的平均精度。还设计了其他实验来调查网络对丢失数据的时间依赖性和性能。误差分析证实了我们提出的网络能够估算季节性和昼夜变化环境下的大气特征，并能够处理缺失数据的影响。预测结果和误差分析都很有希望，并证明我们的网络具有实时提供准确的大气校正和目标探测的能力。