2016
DOI: 10.3390/ijgi5060083
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Morphological Principal Component Analysis for Hyperspectral Image Analysis

Abstract: This article deals with the issue of reducing the spectral dimension of a hyperspectral image using principal component analysis (PCA). To perform this dimensionality reduction, we propose the addition of spatial information in order to improve the features that are extracted. Several approaches proposed to add spatial information are discussed in this article. They are based on mathematical morphology operators. These morphological operators are the area opening/closing, granulometries and grey-scale distance… Show more

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Cited by 16 publications
(12 citation statements)
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“…It can be used to extract useful information regarding objects, such as the shape, skeletons, boundaries, and convex hull. [11] [12][15].…”
Section: (Ii) Morphological Operationsmentioning
confidence: 99%
“…It can be used to extract useful information regarding objects, such as the shape, skeletons, boundaries, and convex hull. [11] [12][15].…”
Section: (Ii) Morphological Operationsmentioning
confidence: 99%
“…Principal Component Analysis (PCA) consists of defining new channels that summarize the information contained in an image in multispectral space [19,20]. This method aims to maximize (statistically) the amount of information (or variance) of original data in a restricted number of components [11,21]. From the bands of Principal Components (PCs), a clear colored composition discriminated the different lithological units, and those same bands are specially used for the automatic extraction of linear structures ( Figure 5).…”
Section: Lithological Mappingmentioning
confidence: 99%
“…In this step, in the process of the data field index calculation, with the help of principal component analysis (PCA) [26], we used the first three principal components of the hyperspectral The accuracy of the anomalous endmember extraction directly affects the accuracy of the spectral unmixing. It is shown in the above figures that when an anomalous pixel is added to a corner of a simulated image, the anomalous pixel will form an obvious extreme in a neighbouring range of the local data field.…”
Section: Data Field Index Calculationmentioning
confidence: 99%
“…In this step, in the process of the data field index calculation, with the help of principal component analysis (PCA) [26], we used the first three principal components of the hyperspectral image and the impact factor σ as the inputs and obtained a set of data field values derived from the input image as the outputs.…”
Section: Data Field Index Calculationmentioning
confidence: 99%