scholarly journals Evaluating visible derivative spectroscopy by varimax-rotated, principal component analysis of aerial hyperspectral images from the western basin of Lake Erie

2019 ◽  
Vol 45 (3) ◽  
pp. 522-535 ◽  
Author(s):  
Joseph D. Ortiz ◽  
Dulci M. Avouris ◽  
Stephen J. Schiller ◽  
Jeffrey C. Luvall ◽  
John D. Lekki ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Lake Erie ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Images ◽  
Western Basin ◽  
Derivative Spectroscopy ◽  
Rotated Principal Component Analysis

scholarly journals SLIC Superpixel-Based l2,1-Norm Robust Principal Component Analysis for Hyperspectral Image Classification

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 479 ◽  
Author(s):  
Baokai Zu ◽  
Kewen Xia ◽  
Tiejun Li ◽  
Ziping He ◽  
Yafang Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Component Analysis ◽  
Curse Of Dimensionality ◽  
Hyperspectral Images ◽  
Robust Principal Component Analysis ◽  
Superpixel Segmentation ◽  
Homogeneous Regions

Hyperspectral Images (HSIs) contain enriched information due to the presence of various bands, which have gained attention for the past few decades. However, explosive growth in HSIs’ scale and dimensions causes “Curse of dimensionality” and “Hughes phenomenon”. Dimensionality reduction has become an important means to overcome the “Curse of dimensionality”. In hyperspectral images, labeled samples are more difficult to collect because they require many labor and material resources. Semi-supervised dimensionality reduction is very important in mining high-dimensional data due to the lack of costly-labeled samples. The promotion of the supervised dimensionality reduction method to the semi-supervised method is mostly done by graph, which is a powerful tool for characterizing data relationships and manifold exploration. To take advantage of the spatial information of data, we put forward a novel graph construction method for semi-supervised learning, called SLIC Superpixel-based l 2 , 1 -norm Robust Principal Component Analysis (SURPCA2,1), which integrates superpixel segmentation method Simple Linear Iterative Clustering (SLIC) into Low-rank Decomposition. First, the SLIC algorithm is adopted to obtain the spatial homogeneous regions of HSI. Then, the l 2 , 1 -norm RPCA is exploited in each superpixel area, which captures the global information of homogeneous regions and preserves spectral subspace segmentation of HSIs very well. Therefore, we have explored the spatial and spectral information of hyperspectral image simultaneously by combining superpixel segmentation with RPCA. Finally, a semi-supervised dimensionality reduction framework based on SURPCA2,1 graph is used for feature extraction task. Extensive experiments on multiple HSIs showed that the proposed spectral-spatial SURPCA2,1 is always comparable to other compared graphs with few labeled samples.

scholarly journals A three-dimensional principal component analysis approach for exploratory analysis of hyperspectral data: identification of ovarian cancer samples based on Raman microspectroscopy imaging of blood plasma

The Analyst ◽  
2019 ◽  
Vol 144 (7) ◽  
pp. 2312-2319 ◽  
Author(s):  
Camilo L. M. Morais ◽  
Pierre L. Martin-Hirsch ◽  
Francis L. Martin
Keyword(s):  
Ovarian Cancer ◽  
Principal Component Analysis ◽  
Blood Plasma ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Data ◽  
Exploratory Analysis ◽  
Hyperspectral Images ◽  
Analysis Approach

Three-dimensional principal component analysis (3D-PCA) for exploratory analysis of hyperspectral images.

scholarly journals HYPERSPECTRAL IMAGE DENOISING USING A NONLOCAL SPECTRAL SPATIAL PRINCIPAL COMPONENT ANALYSIS

2018 ◽  
Vol XLII-3 ◽  
pp. 789-794 ◽  
Author(s):  
D. Li ◽  
L. Xu ◽  
J. Peng ◽  
J. Ma
Keyword(s):  
Principal Component Analysis ◽  
Spatial Structure ◽  
Hyperspectral Image ◽  
Negative Impact ◽  
Principal Component ◽  
Component Analysis ◽  
Research Area ◽  
Hyperspectral Images ◽  
Spectral Correlation ◽  
Nonlocal Similarity

Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classification and so on. In this paper, we develop a noise reduction method based on principal component analysis (PCA) for hyperspectral imagery, which is dependent on the assumption that the noise can be removed by selecting the leading principal components. The main contribution of paper is to introduce the spectral spatial structure and nonlocal similarity of the HSIs into the PCA denoising model. PCA with spectral spatial structure can exploit spectral correlation and spatial correlation of HSI by using 3D blocks instead of 2D patches. Nonlocal similarity means the similarity between the referenced pixel and other pixels in nonlocal area, where Mahalanobis distance algorithm is used to estimate the spatial spectral similarity by calculating the distance in 3D blocks. The proposed method is tested on both simulated and real hyperspectral images, the results demonstrate that the proposed method is superior to several other popular methods in HSI denoising.

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