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.

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 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.

Sparse Representation Based on Principal Component for Compressed Sensing Hyperspectral Images

2013 ◽  
Vol 321-324 ◽  
pp. 1154-1157
Author(s):  
Bin Zhou ◽  
Kun Li ◽  
Guang Xu
Keyword(s):  
Principal Component Analysis ◽  
Compressed Sensing ◽  
Sparse Representation ◽  
Information Acquisition ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Component Analysis ◽  
Theoretical Framework ◽  
Hyperspectral Images ◽  
Compressed Sampling

Compressed sensing (CS) is a new developed theoretical framework for information acquisition and processing that breaks through the conventional Nyquist sampling limit. This paper proposes a sparse representation schemes based on principal component analysis (PCA) for CS that will be used for hyperspectral images compressed sampling. This scheme employs the prediction transform matrix to remove the correlations among successive hyperspectral measurement vectors. Experiment processes using the hyperspectral image from Earth Observing One (EO-1), and it shows a desired result both at reconstruction and denoising.

Chemical Structure-Odor Correlation in a Series of Synthetic n-Nonen-1-ols

1995 ◽  
Vol 50 (11-12) ◽  
pp. 757-765 ◽  
Author(s):  
Yasunobu Sakoda ◽  
Kenji Matsui ◽  
Tadahiko Kajiwara ◽  
Akikazu Hatanaka
Keyword(s):  
Principal Component Analysis ◽  
Double Bond ◽  
Spatial Structure ◽  
Chemical Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Acetylenic Compound ◽  
Analysis Method ◽  
Entire Series ◽  
Novel Method

In order to elucidate chemical structure-odor correlation in the all isomers of n-nonen-1- ols, an entire series of these alcohols were synthesized stereo-selectively in high purity. For unequivocal syntheses of them, geometrically selective hydrogenation of the respective acetylenic compound was adopted. The synthesized alcohols were converted to their 3,5-dinitrobenzoate derivatives with 3,5-dinitrobenzoyl chloride, and then purified by repeated recrystallization. Chemical structure-odor correlations in all the isomers of n-nonen-1-ols were elucidated by introducing a novel method to evaluate odor characteristics and by treating the obtained data statistically with the principal component analysis method (Cramer et al., 1988). The odor profiles of the tested compounds were attributable largely to the positions of the carbon- double bond. The geometries of compounds had only a little effect. With the principal component analysis, the odor profiles of the series of compounds were successfully integrated into the first and the second principal components. The first component (PC-1) consisted of combined characteristics of fruity, fresh, sweet, herbal and oily-fatty, in which herbal and oily-fatty were conversely correlated each other to the position of double-bond of the tested compounds. Of these, only (6Z)-nonen-1-ol deviated markedly from the correlation, indicative of some special interaction between the spatial structure of this compound and the sensory machinery of human.

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