用UNet加速火星矿物识别的光谱预处理，效率提升18倍。
A UNet Model for Accelerated Preprocessing of CRISM Hyperspectral Data for Mineral Identification on Mars
- 基于UNet的自编码器自动完成平滑与连续背景去除
- 800x800场景预处理时间从1.5小时缩短至5分钟
- 适合需要高效火星矿物制图的研究者
准确识别火星表面矿物对理解其地质历史至关重要。本文提出一种基于UNet的自编码器模型，用于高效处理CRISM MTRDR高光谱数据的光谱预处理，克服传统方法计算量大、耗时长的缺点。该模型自动完成平滑与连续背景去除等关键步骤，同时保留重要矿物吸收特征。模型在MICA光谱库增强数据上训练，引入真实变异以模拟MTRDR数据条件。集成该框架后，一个800x800的MTRDR场景预处理时间由1.5小时降至5分钟（在NVIDIA T1600 GPU上）。预处理后的光谱使用MICAnet进行分类。在标注的CRISM TRDR数据上评估表明，该方法在保持竞争性分类精度的同时，显著提升预处理效率。本工作展示了基于UNet的预处理框架在提升火星矿物制图速度与可靠性方面的潜力。
原文摘要 · Abstract (English)
Accurate mineral identification on the Martian surface is critical for understanding the planet's geological history. This paper presents a UNet-based autoencoder model for efficient spectral preprocessing of CRISM MTRDR hyperspectral data, addressing the limitations of traditional methods that are computationally intensive and time-consuming. The proposed model automates key preprocessing steps, such as smoothing and continuum removal, while preserving essential mineral absorption features. Trained on augmented spectra from the MICA spectral library, the model introduces realistic variability to simulate MTRDR data conditions. By integrating this framework, preprocessing time for an 800x800 MTRDR scene is reduced from 1.5 hours to just 5 minutes on an NVIDIA T1600 GPU. The preprocessed spectra are subsequently classified using MICAnet, a deep learning model for Martian mineral identification. Evaluation on labeled CRISM TRDR data demonstrates that the proposed approach achieves competitive accuracy while significantly enhancing preprocessing efficiency. This work highlights the potential of the UNet-based preprocessing framework to improve the speed and reliability of mineral mapping on Mars.
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