• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Noise Simulation and Correction in Synthetic Airborne TIR Data for Mineral Quantification
【24h】

Noise Simulation and Correction in Synthetic Airborne TIR Data for Mineral Quantification

机译：合成机载TIR数据中的噪声模拟和校正，用于矿物定量

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Rock-forming minerals (such as feldspar and quartz) can be identified and quantified from thermal infrared (TIR) laboratory spectroscopy using spectral models. This paper uses synthetic airborne TIR spectra to test whether the hyperspectral Spatially Enhanced Broadband Array Spectrograph System (SEBASS) would theoretically be able to detect quartz and feldspar minerals and quantitatively predict mineral modes in felsic igneous rocks. Data from a previous laboratory study were used to simulate TIR spectra with band locations and noise levels of the SEBASS sensor. The quantitative partial least squares regression (PLSR) models from that study were applied to newly created synthetic SEBASS data, and results were compared with the predictions from the previous study. Predicted compositions based on SEBASS band positions are nearly identical to those based on laboratory resolution. Results are still reliable [prediction errors within 0.4% (absolute)] to the original laboratory PLSR predictions when adding up to 1% noise (about five times the SEBASS noise level) to the synthetic data. Prediction errors rapidly increase when noise levels beyond 1% are used. These results show that SEBASS' spectral resolution, spectral coverage, and signal-to-noise levels are sufficient to quantitatively predict quartz and feldspar amounts, and feldspar compositions with models based on PLSR. Spectral distortions, such as reduced spectral contrast, tilts, and vertical shifts, must be compensated for before these quantitative models are applied. A mean and standard deviation (MASD) normalization is proposed using a set of ground data for compensating systematic errors that are common to all image pixels.
机译：可以使用光谱模型从热红外（TIR）实验室光谱学中识别和量化成岩矿物（例如长石和石英）。本文使用合成的机载TIR光谱来测试高光谱空间增强宽带阵列光谱仪系统（SEBASS）在理论上是否能够检测石英和长石矿物并定量预测长英质火成岩中的矿物模式。来自先前实验室研究的数据用于模拟TIR光谱以及SEBASS传感器的波段位置和噪声水平。该研究的定量偏最小二乘回归（PLSR）模型应用于新创建的合成SEBASS数据，并将结果与​​先前研究的预测结果进行了比较。基于SEBASS谱带位置的预测组成与基于实验室分辨率的预测组成几乎相同。当对合成数据添加高达1％的噪声（大约是SEBASS噪声水平的五倍）时，结果仍比原始实验室PLSR预测可靠[预测误差在0.4％（绝对）之内]。当使用超过1％的噪声水平时，预测误差会迅速增加。这些结果表明，使用基于PLSR的模型，SEBASS的光谱分辨率，光谱覆盖范围和信噪比水平足以定量预测石英和长石的量以及长石的成分。在应用这些定量模型之前，必须补偿光谱失真，例如降低的光谱对比度，倾斜度和垂直偏移。使用一组地面数据来提出均值和标准差（MASD）归一化，以补偿所有图像像素共有的系统误差。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号