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HyperHazeOff:高光谱远程传感图像去的基准

Artem Nikonorov¹, Dmitry Sidorchuk², Nikita Odinets²

¹Samara National Research University, Moskovskoye Shosse 34, 443086 Samara, Russia.

Journal of imaging

|December 24, 2025

概括

一个新的基准,HyperHazeOff,解决了对超光谱脱气的现实数据的缺乏. 它可以进行更好的评估,并表明在合成数据上训练的模型更好地将真实雾概括起来.

科学领域:

遥感遥感遥感遥感
图像处理图像处理
环境科学环境科学

背景情况:

超光谱遥感图像 (HSI) 对环境和农业监测至关重要.
大气雾降低了HSI,扭曲了空间和光谱信息并阻碍了分析.
现有的超光谱除雾研究缺乏对应的真基准,限制了公平的评估和概括.

研究的目的:

推出HyperHazeOff,这是超光谱脱气的第一个全面基准.
为数据,任务和评估协议提供一个统一的平台,用于超光谱除研究.
促进可重现的研究,并推进高光谱图像修复领域.

主要方法:

该基准包括RRealHyperPDID (110个配对的真实模糊/无模糊的HSI场景) 和RSyntHyperPDID (2616个合成配对样本).
物理接地雾形成模型被用于合成数据生成.
为下游任务评估提供了农田划界和土地分类的注释.

主要成果:

在现有数据集上训练的最先进的超光谱模型无法将其推广到现实世界.
在HyperHazeOff中对合成数据集 (RSyntHyperPDID) 的培训显著改善了AACNet的真实雾恢复.
HyperHazeOff为高光谱脱气建立了可重复的基线.

结论:

由于其全面的性质和现实世界的数据,HyperHazeOff对于推进高光谱除研究至关重要.
该基准表明了当前模型的局限性,并强调了现实的培训数据的重要性.
HyperHazeOff的开放可用性促进了可重复的研究,并促进了更强大的超光谱除技术的开发.

关键词:

农业领域的界限划定拆除房屋的基准指标超光谱图像 (HSI) 是一种超光谱图像.土地分类土地分类在现实世界中,除草是真实的.远程传感是一种遥感技术.

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