The influence of principal component analysis on the spatial structure of a multispectral dataset.
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| Title: | The influence of principal component analysis on the spatial structure of a multispectral dataset. |
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| Authors: | Avena, G. C., Ricotta, C., Volpe, F. |
| Source: | International Journal of Remote Sensing. 11/15/99, Vol. 20 Issue 17, p3367-3376. 10p. 2 Black and White Photographs, 2 Charts, 2 Graphs. |
| Subjects: | Principal components analysis, Remote-sensing images |
| Abstract: | Multispectral remote sensing images often have extensive interband correlation. As a result, the images may contain similar information and have similar spatial structure. Principal component analysis (PCA) is a technique for removing or reducing the duplication or redundancy in multispectral images and for compressing all of the information that is contained in an original n-channel set of multispectral images into less than n channels or, more specifically, to their principal components. These are then used instead of the original data for image analysis and interpretation. The principal components are ranked in terms of the amount of variance that they explain. A consequence of ranking in this way is that the resulting principal components showa markedly different spatial structure from one another. This effect can be problematical, for example, when studying landscape ecology, where understanding the interactions between elements of the landscape structure as manifest in remote sensing images and environmental processes is of primary importance. Although the difference in spatial structure of an image after applying PCA and its influence on potential applications have been known for some time, it does not appear to have been studied explicitly. Accordingly, the aim of this paper was to examine the implications of applying PCA for the spatial structure and content of multispectral remote sensing images using parts of a Landsat Thematic Mapper (TM) frame of northern Sardinia, Italy. The results show that, due to the significant influence of PCA on the spatial structure andcontent of remote sensing images, the resulting principal components have a spatial structure and content that differ markedly from one another and from the original images. As a result, extreme care is necessary when applying PCA to remote sensing images and interpreting the results. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 3860507 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: The influence of principal component analysis on the spatial structure of a multispectral dataset. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Avena%2C+G%2E+C%2E%22">Avena, G. C.</searchLink><br /><searchLink fieldCode="AR" term="%22Ricotta%2C+C%2E%22">Ricotta, C.</searchLink><br /><searchLink fieldCode="AR" term="%22Volpe%2C+F%2E%22">Volpe, F.</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Remote+Sensing%22">International Journal of Remote Sensing</searchLink>. 11/15/99, Vol. 20 Issue 17, p3367-3376. 10p. 2 Black and White Photographs, 2 Charts, 2 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Multispectral remote sensing images often have extensive interband correlation. As a result, the images may contain similar information and have similar spatial structure. Principal component analysis (PCA) is a technique for removing or reducing the duplication or redundancy in multispectral images and for compressing all of the information that is contained in an original n-channel set of multispectral images into less than n channels or, more specifically, to their principal components. These are then used instead of the original data for image analysis and interpretation. The principal components are ranked in terms of the amount of variance that they explain. A consequence of ranking in this way is that the resulting principal components showa markedly different spatial structure from one another. This effect can be problematical, for example, when studying landscape ecology, where understanding the interactions between elements of the landscape structure as manifest in remote sensing images and environmental processes is of primary importance. Although the difference in spatial structure of an image after applying PCA and its influence on potential applications have been known for some time, it does not appear to have been studied explicitly. Accordingly, the aim of this paper was to examine the implications of applying PCA for the spatial structure and content of multispectral remote sensing images using parts of a Landsat Thematic Mapper (TM) frame of northern Sardinia, Italy. The results show that, due to the significant influence of PCA on the spatial structure andcontent of remote sensing images, the resulting principal components have a spatial structure and content that differ markedly from one another and from the original images. As a result, extreme care is necessary when applying PCA to remote sensing images and interpreting the results. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/014311699211381 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 3367 Subjects: – SubjectFull: Principal components analysis Type: general – SubjectFull: Remote-sensing images Type: general Titles: – TitleFull: The influence of principal component analysis on the spatial structure of a multispectral dataset. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Avena, G. C. – PersonEntity: Name: NameFull: Ricotta, C. – PersonEntity: Name: NameFull: Volpe, F. IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 11 Text: 11/15/99 Type: published Y: 1999 Identifiers: – Type: issn-print Value: 01431161 Numbering: – Type: volume Value: 20 – Type: issue Value: 17 Titles: – TitleFull: International Journal of Remote Sensing Type: main |
| ResultId | 1 |
