Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images

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Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images

机译：基于非线性主成分分析的光谱变换用于高光谱图像降维

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《European Journal of Remote Sensing》 |2018年第1期|共页
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1. Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images [J] . a European Journal of Remote Sensing . 2018,第1期

机译：基于非线性主成分分析的光谱变换用于高光谱图像降维
2. Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery [J] . Binol Hamidullah Mathematical Problems in Engineering . 2018,第PTa11期

机译：基于集成学习的多核主成分分析用于高光谱图像降维和分类
3. Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery [J] . Hamidullah Binol Mathematical Problems in Engineering: Theory, Methods and Applications . 2018,第5期

机译：基于集成学习的多核主成分分析用于高光谱图像降维和分类
4. DIMENSIONALITY REDUCTION FOR HYPERSPECTRAL IMAGES TO IMPROVE OBJECT-BASED IMAGE CLASSIFICATION USING FEATURE SELECTION AND PRINCIPAL COMPONENTS ANALYSIS [C] . Taskin Kavzoglu, Hasan Tonbul, Ismail Colkesen Asian conference on remote sensing;ACRS . 2018

机译：利用特征选择和主成分分析减少超光谱图像的维数以改进基于对象的图像分类
5. Limitations of principal component analysis for dimensionality-reduction for classification of hyperspectral data. [D] . Cheriyadat, Anil. 2003

机译：用于高光谱数据分类的降维主成分分析的局限性。
6. Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding [O] . Hong Liu, Kewen Xia, Tiejun Li, 2020

机译：基于改进的空间光谱重量歧管嵌入的高光谱图像的维数减少
7. Randomized Nonlinear Component Analysis for Dimensionality Reduction of Hyperspectral Images [O] . Damodaran, Bharath,, Courty, Nicolas, Tavenard, Romain 2017

机译：高光谱图像降维的随机非线性成分分析
8. Reconstruction Error and Principal Component Based Anomaly Detection in Hyperspectral Imagery. [R] . Jablonski, J. A. 2014

机译：基于重构误差和主成分的高光谱图像异常检测。

Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images

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