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Reconstruction Error and Principal Component Based Anomaly Detection in Hyperspectral Imagery.

机译：基于重构误差和主成分的高光谱图像异常检测。

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Jablonski, J. A.;
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年度 2014
页码 1-141
总页数 141
原文格式 PDF
正文语种 eng
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1. Principal Component Reconstruction Error for Hyperspectral Anomaly Detection [J] . Jablonski James /A/., Bihl Trevor J., Bauer Kenneth W. Geoscience and Remote Sensing Letters, IEEE . 2015,第8期

机译：高光谱异常检测的主成分重建误差
2. Randomized subspace-based robust principal component analysis for hyperspectral anomaly detection [J] . Sun Weiwei, Yang Gang, Li Jialin, Journal of Applied Remote Sensing . 2018,第1期

机译：基于随机的SubsPace的高光谱异常检测鲁棒主成分分析
3. Detection of Anomalies Produced by Buried Archaeological Structures Using Nonlinear Principal Component Analysis Applied to Airborne Hyperspectral Image [J] . Cavalli R.M., Licciardi G.A., Chanussot J. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of . 2013,第2aPart2期

机译：非线性主成分分析在机载高光谱图像中探测埋藏考古结构异常
4. Hyperspectral anomaly detection using Modified Principal component analysis reconstruction error [C] . Maryam Vafadar, Hassan Ghassemian Iranian Conference on Electrical Engineering . 2017

机译：基于修正主成分分析重建误差的高光谱异常检测
5. A performance characterization of kernel-based algorithms for anomaly detection in hyperspectral imagery. [D] . Goldberg, Hirsh R. 2007

机译：基于内核的高光谱图像异常检测算法的性能表征。
6. Principal component analysis of hyperspectral data for early detection of mould in cheeselets [O] . Jessica Farrugia, Sholeem Griffin, Vasilis P. Valdramidis, 2021

机译：高光谱数据的主要成分分析用于在CheSESelets中早期检测模具
7. Detection of Anomalies Produced by Buried Archaeological Structures Using Nonlinear Principal Component Analysis Applied to Airborne Hyperspectral Image [O] . Cavalli, Rosa Maria, Licciardi, Giorgio, Chanussot, Jocelyn 2013

机译：非线性主成分分析在机载高光谱图像中探测埋藏考古结构异常

Reconstruction Error and Principal Component Based Anomaly Detection in Hyperspectral Imagery.

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