Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial basis function networks.
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| Title: | Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial basis function networks. |
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| Authors: | Mokhtari, Mohammad Hossein1 (AUTHOR) mh.mokhtari@yazd.ac.ir, Deilami, Kaveh2 (AUTHOR), Moosavi, Vahid3 (AUTHOR) |
| Source: | Earth Science Informatics. Jun2020, Vol. 13 Issue 2, p493-507. 15p. |
| Subject Terms: | *Radial basis functions, *Surface of the earth, *High resolution imaging, *Artificial neural networks, *Principal components analysis, *Inspection & review, *Canonical correlation (Statistics), *Histograms |
| Geographic Terms: | Tehran (Iran) |
| Abstract: | The deactivation of Earth Observing-1 satellite has resulted in the termination of capturing Hyperion data as a unique source of hyperspectral satellite imagery. These images also were collected through an on-demand service and thereby are not available for the entire Earth's surface. The Operational Land Imager (OLI) sensor, on the other hand, provides a free source of multi-spectral images with global coverage. Recognized these facts, the aim of this paper is to enhance the spectral resolution of OLI images by using existing Hyperion imageries to generate a high spectral resolution image for a desired date and site. This was conducted through the artificial neural network (ANN). To find the suitable ANN, we compared the performance of multilayer perceptron (MLP) and radial basis function (RBF) networks for spectral enhancement. The research obtained two Hyperion and OLI images covering West Region of Tehran, Iran, on 4 January 2016. From 242 original Hyperion spectral bands, we selected 31 bands to reproduce from OLI spectral bands. These were determined through visual inspection, principal component analysis and Pearson's correlation test. The MLP and RBF networks were generated based on the OLI bands 1–7 and per 31 Hyperion bands as input and output layers respectively. The comparison between the spectral bands of spectra-enhanced image and original Hyperion data indicated a good agreement (0.884 > R2 > 0.692). This study also found MLP network delivered higher accuracy against RBF network for spectral enhancement. The spectra-enhanced image can be used in studies with the need of images with continuous spectral bands. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 143221025 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial basis function networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mokhtari%2C+Mohammad+Hossein%22">Mokhtari, Mohammad Hossein</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mh.mokhtari@yazd.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Deilami%2C+Kaveh%22">Deilami, Kaveh</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Moosavi%2C+Vahid%22">Moosavi, Vahid</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Earth+Science+Informatics%22">Earth Science Informatics</searchLink>. Jun2020, Vol. 13 Issue 2, p493-507. 15p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Radial+basis+functions%22">Radial basis functions</searchLink><br />*<searchLink fieldCode="DE" term="%22Surface+of+the+earth%22">Surface of the earth</searchLink><br />*<searchLink fieldCode="DE" term="%22High+resolution+imaging%22">High resolution imaging</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Inspection+%26+review%22">Inspection & review</searchLink><br />*<searchLink fieldCode="DE" term="%22Canonical+correlation+%28Statistics%29%22">Canonical correlation (Statistics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Histograms%22">Histograms</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Tehran+%28Iran%29%22">Tehran (Iran)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The deactivation of Earth Observing-1 satellite has resulted in the termination of capturing Hyperion data as a unique source of hyperspectral satellite imagery. These images also were collected through an on-demand service and thereby are not available for the entire Earth's surface. The Operational Land Imager (OLI) sensor, on the other hand, provides a free source of multi-spectral images with global coverage. Recognized these facts, the aim of this paper is to enhance the spectral resolution of OLI images by using existing Hyperion imageries to generate a high spectral resolution image for a desired date and site. This was conducted through the artificial neural network (ANN). To find the suitable ANN, we compared the performance of multilayer perceptron (MLP) and radial basis function (RBF) networks for spectral enhancement. The research obtained two Hyperion and OLI images covering West Region of Tehran, Iran, on 4 January 2016. From 242 original Hyperion spectral bands, we selected 31 bands to reproduce from OLI spectral bands. These were determined through visual inspection, principal component analysis and Pearson's correlation test. The MLP and RBF networks were generated based on the OLI bands 1–7 and per 31 Hyperion bands as input and output layers respectively. The comparison between the spectral bands of spectra-enhanced image and original Hyperion data indicated a good agreement (0.884 > R2 > 0.692). This study also found MLP network delivered higher accuracy against RBF network for spectral enhancement. The spectra-enhanced image can be used in studies with the need of images with continuous spectral bands. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=143221025 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s12145-020-00451-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 493 Subjects: – SubjectFull: Radial basis functions Type: general – SubjectFull: Surface of the earth Type: general – SubjectFull: High resolution imaging Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Principal components analysis Type: general – SubjectFull: Inspection & review Type: general – SubjectFull: Canonical correlation (Statistics) Type: general – SubjectFull: Histograms Type: general – SubjectFull: Tehran (Iran) Type: general Titles: – TitleFull: Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial basis function networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mokhtari, Mohammad Hossein – PersonEntity: Name: NameFull: Deilami, Kaveh – PersonEntity: Name: NameFull: Moosavi, Vahid IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 18650473 Numbering: – Type: volume Value: 13 – Type: issue Value: 2 Titles: – TitleFull: Earth Science Informatics Type: main |
| ResultId | 1 |
