基于集合的Transformer用于远距离长波红外高光谱成像中的大气补偿

摘要

被动长波红外（LWIR）远距离高光谱成像依赖于大气吸收与发射以及反射辐射，因此大气补偿对于获取目标信息至关重要。尽管其重要性显著，但由于实践和建模的难度，这一补偿过程在很大程度上被忽视。本文提出了一种轻量级的基于集合的深度学习框架，该框架以在不同远距离距离处采集的多个辐射测量值作为输入，联合估计透射率、大气路径辐射以及共享的下行辐射光谱。我们通过稀疏自编码器分析学习到的表示，并观察到，尽管没有位置监督，但某些潜在特征确实在测试数据的地理相干子集上被激活。在MODTRAN生成的远距离LWIR数据集上的实验表明，所有估计产物的光谱失真较低。数据集和代码已公开：https://factral.co/SAE-LWIR/

English

Passive long-wave infrared (LWIR) hyperspectral imaging under a standoff geometry depends on atmospheric absorption and emission, as well as reflected radiance, thus making atmospheric compensation essential to get knowledge of a target of interest. Despite its importance, this compensation has been largely overlooked due to its practical and modeling difficulty. In this paper, we present a lightweight set-based deep learning framework that takes multiple radiance measurements, collected at different standoff ranges, as input and jointly estimates transmittance, atmospheric path radiance, and a shared downwelling spectrum. We analyze the learned representation with a sparse autoencoder and observe that several latent features do activate on geographically coherent subsets of the test data despite the absence of location supervision. Experiments on a MODTRAN generated standoff LWIR dataset demonstrate low spectral distortion across all estimated products. The dataset and code is publicly available at: https://factral.co/SAE-LWIR/