Curvilinear component analysis for nonlinear dimensionality reduction of hyperspectral images

2002 ◽  
Author(s):  
Marc Lennon ◽  
Gregoire Mercier ◽  
Marie-Catherine Mouchot ◽  
Laurence Hubert-Moy
Keyword(s):  
Dimensionality Reduction ◽  
Component Analysis ◽  
Hyperspectral Images ◽  
Nonlinear Dimensionality Reduction

scholarly journals An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images

2019 ◽  
Vol 11 (2) ◽  
pp. 136 ◽  
Author(s):  
Yuliang Wang ◽  
Huiyi Su ◽  
Mingshi Li
Keyword(s):  
Feature Extraction ◽  
Logistic Regression ◽  
Dimensionality Reduction ◽  
Spectral Feature ◽  
Hyperspectral Images ◽  
Nonlinear Dimensionality Reduction ◽  
Impervious Surfaces ◽  
Urban Impervious Surfaces ◽  
Improved Model ◽  
Logistic Regression Classifier

Hyperspectral images (HSIs) provide unique capabilities for urban impervious surfaces (UIS) extraction. This paper proposes a multi-feature extraction model (MFEM) for UIS detection from HSIs. The model is based on a nonlinear dimensionality reduction technique, t-distributed stochastic neighbor embedding (t-SNE), and the deep learning method convolutional deep belief networks (CDBNs). We improved the two methods to create a novel MFEM consisting of improved t-SNE, deep compression CDBNs (d-CDBNs), and a logistic regression classifier. The improved t-SNE method provides dimensionality reduction and spectral feature extraction from the original HSIs and the d-CDBNs algorithm extracts spatial feature and edges using the reduced dimensional datasets. Finally, the extracted features are combined into multi-feature for the impervious surface detection using the logistic regression classifier. After comparing with the commonly used methods, the current experimental results demonstrate that the proposed MFEM model provides better performance for UIS extraction and detection from HSIs.

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 ENSO dynamics in current climate models: an investigation using nonlinear dimensionality reduction

2008 ◽  
Vol 15 (2) ◽  
pp. 339-363 ◽  
Author(s):  
I. Ross ◽  
P. J. Valdes ◽  
S. Wiggins
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Dimensionality Reduction ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Principal Component ◽  
Component Analysis ◽  
Nonlinear Dimensionality Reduction ◽  
Climate Data

Abstract. Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These linear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of low-dimensional manifolds in climate data sets arising from nonlinear dynamics. Here, we apply Isomap, one such technique, to the study of El Niño/Southern Oscillation variability in tropical Pacific sea surface temperatures, comparing observational data with simulations from a number of current coupled atmosphere-ocean general circulation models. We use Isomap to examine El Niño variability in the different datasets and assess the suitability of the Isomap approach for climate data analysis. We conclude that, for the application presented here, analysis using Isomap does not provide additional information beyond that already provided by principal component analysis.

scholarly journals Geometrical Approximated Principal Component Analysis for Hyperspectral Image Analysis

2020 ◽  
Vol 12 (11) ◽  
pp. 1698 ◽  
Author(s):  
Alina L. Machidon ◽  
Fabio Del Frate ◽  
Matteo Picchiani ◽  
Octavian M. Machidon ◽  
Petre L. Ogrutan
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Images ◽  
Land Classification ◽  
Hyperspectral Image Processing ◽  
Hyperspectral Image Analysis ◽  
Effective Dimensionality

Principal Component Analysis (PCA) is a method based on statistics and linear algebra techniques, used in hyperspectral satellite imagery for data dimensionality reduction required in order to speed up and increase the performance of subsequent hyperspectral image processing algorithms. This paper introduces the PCA approximation method based on a geometric construction approach (gaPCA) method, an alternative algorithm for computing the principal components based on a geometrical constructed approximation of the standard PCA and presents its application to remote sensing hyperspectral images. gaPCA has the potential of yielding better land classification results by preserving a higher degree of information related to the smaller objects of the scene (or to the rare spectral objects) than the standard PCA, being focused not on maximizing the variance of the data, but the range. The paper validates gaPCA on four distinct datasets and performs comparative evaluations and metrics with the standard PCA method. A comparative land classification benchmark of gaPCA and the standard PCA using statistical-based tools is also described. The results show gaPCA is an effective dimensionality-reduction tool, with performance similar to, and in several cases, even higher than standard PCA on specific image classification tasks. gaPCA was shown to be more suitable for hyperspectral images with small structures or objects that need to be detected or where preponderantly spectral classes or spectrally similar classes are present.

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