用多距离测量数据，联合估计红外成像中的大气影响。
Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging
- 基于多个观测距离的辐射数据，构建轻量级集合模型
- 估计结果在所有产品上均显示低光谱失真
- 适合遥感、红外成像领域研究人员使用
远距离被动长波红外（LWIR）高光谱成像依赖于大气吸收与发射以及反射辐射，因此大气补偿对获取目标信息至关重要。尽管如此，由于实际操作和建模困难，该问题长期被忽视。本文提出一种轻量级集合型深度学习框架，以不同距离下采集的多组辐射测量值为输入，联合估计透射率、大气路径辐射及共享的地向下辐射光谱。通过稀疏自编码器分析学习到的表征，发现即使无位置监督，部分潜在特征仍对测试数据中地理上连贯的子集有激活响应。在MODTRAN生成的远距离LWIR数据集上的实验表明，所有估计产物均表现出低光谱失真。数据集与代码已公开：https://factral.co/SAE-LWIR/
原文摘要 · Abstract (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/
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