Hyperspectral images (HSI) have many applications, ranging from environmental monitoring to national security, and can be used for material detection and identification. Longwave infrared (LWIR) HSI can be used for gas plume detection and analysis. Oftentimes, only a few images of a scene of interest are available and are analyzed individually. The ability to combine information from multiple images into a single, cohesive representation could enhance analysis by providing more context on the scene's geometry and spectral properties. Neural radiance fields (NeRFs) create a latent neural representation of volumetric scene properties that enable novel-view rendering and geometry reconstruction, offering a promising avenue for hyperspectral 3D scene reconstruction. We explore the possibility of using NeRFs to create 3D scene reconstructions from LWIR HSI and demonstrate that the model can be used for the basic downstream analysis task of gas plume detection. The physics-based DIRSIG software suite was used to generate a synthetic multi-view LWIR HSI dataset of a simple facility with a strong sulfur hexafluoride gas plume. Our method, built on the standard Mip-NeRF architecture, combines state-of-the-art methods for hyperspectral NeRFs and sparse-view NeRFs, along with a novel adaptive weighted MSE loss. Our final NeRF method requires around 50% fewer training images than the standard Mip-NeRF and achieves an average PSNR of 39.8 dB with as few as 30 training images. Gas plume detection applied to NeRF-rendered test images using the adaptive coherence estimator achieves an average AUC of 0.821 when compared with detection masks generated from ground-truth test images.

翻译：高光谱图像在环境监测和国家安全等诸多领域具有广泛应用，可用于材料探测与识别。长波红外高光谱图像特别适用于气体羽流的探测与分析。在实际应用中，通常仅能获取少量感兴趣场景的图像，且需进行独立分析。将多幅图像信息融合为统一连贯的表征能力，可通过提供更丰富的场景几何结构与光谱特性上下文信息来增强分析效果。神经辐射场通过创建场景体属性的隐式神经表征，实现了新视角渲染与几何重建，为高光谱三维场景重建提供了极具前景的技术路径。本研究探索了利用神经辐射场从长波红外高光谱图像构建三维场景重建的可能性，并验证了该模型可应用于气体羽流探测这一基础下游分析任务。基于物理机理的DIRSIG软件套件被用于生成包含强六氟化硫气体羽流的简易设施合成多视角长波红外高光谱数据集。本方法基于标准Mip-NeRF架构，融合了高光谱神经辐射场与稀疏视角神经辐射场的前沿技术，并引入新型自适应加权均方误差损失函数。最终提出的神经辐射场方法相比标准Mip-NeRF减少约50%的训练图像需求，仅用30张训练图像即可实现39.8 dB的平均峰值信噪比。采用自适应相干估计器对神经辐射场渲染测试图像进行气体羽流检测，与真实测试图像生成的检测掩模相比，平均曲线下面积达到0.821。