Semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features.
Identifieur interne : 002058 ( Main/Exploration ); précédent : 002057; suivant : 002059Semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features.
Auteurs : Luis Garcia-Torres [Espagne] ; Juan J. Caballero-Novella [Espagne] ; David G Mez-Cand N [Espagne] ; Ana Isabel De-Castro [Espagne]Source :
- PloS one [ 1932-6203 ] ; 2014.
Descripteurs français
- KwdFr :
- MESH :
- anatomie et histologie : Produits agricoles.
- Agriculture, Automatisation, Facteurs temps, Imagerie tridimensionnelle, Logiciel, Normes de référence, Technologie de télédétection.
English descriptors
- KwdEn :
- MESH :
- anatomy & histology : Crops, Agricultural.
- Agriculture, Automation, Imaging, Three-Dimensional, Reference Standards, Remote Sensing Technology, Software, Time Factors.
Abstract
A procedure to achieve the semi-automatic relative image normalization of multitemporal remote images of an agricultural scene called ARIN was developed using the following procedures: 1) defining the same parcel of selected vegetative pseudo-invariant features (VPIFs) in each multitemporal image; 2) extracting data concerning the VPIF spectral bands from each image; 3) calculating the correction factors (CFs) for each image band to fit each image band to the average value of the image series; and 4) obtaining the normalized images by linear transformation of each original image band through the corresponding CF. ARIN software was developed to semi-automatically perform the ARIN procedure. We have validated ARIN using seven GeoEye-1 satellite images taken over the same location in Southern Spain from early April to October 2010 at an interval of approximately 3 to 4 weeks. The following three VPIFs were chosen: citrus orchards (CIT), olive orchards (OLI) and poplar groves (POP). In the ARIN-normalized images, the range, standard deviation (s. d.) and root mean square error (RMSE) of the spectral bands and vegetation indices were considerably reduced compared to the original images, regardless of the VPIF or the combination of VPIFs selected for normalization, which demonstrates the method's efficacy. The correlation coefficients between the CFs among VPIFs for any spectral band (and all bands overall) were calculated to be at least 0.85 and were significant at P = 0.95, indicating that the normalization procedure was comparably performed regardless of the VPIF chosen. ARIN method was designed only for agricultural and forestry landscapes where VPIFs can be identified.
DOI: 10.1371/journal.pone.0091275
PubMed: 24604031
PubMed Central: PMC3946354
Affiliations:
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Caballero-Novella</name><affiliation wicri:level="1"><nlm:affiliation>Institute for Sustainable Agriculture (IAS), Spanish Council for Scientific Research (CSIC), Cordoba, Spain.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Institute for Sustainable Agriculture (IAS), Spanish Council for Scientific Research (CSIC), Cordoba</wicri:regionArea><wicri:noRegion>Cordoba</wicri:noRegion></affiliation></author><author><name sortKey="G Mez Cand N, David" sort="G Mez Cand N, David" uniqKey="G Mez Cand N D" first="David" last="G Mez-Cand N">David G Mez-Cand N</name><affiliation wicri:level="1"><nlm:affiliation>Institute for Sustainable Agriculture (IAS), Spanish Council for Scientific Research (CSIC), Cordoba, Spain.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Institute for Sustainable Agriculture (IAS), Spanish Council for Scientific Research (CSIC), Cordoba</wicri:regionArea><wicri:noRegion>Cordoba</wicri:noRegion></affiliation></author><author><name sortKey="De Castro, Ana Isabel" sort="De Castro, Ana Isabel" uniqKey="De Castro A" first="Ana Isabel" last="De-Castro">Ana Isabel De-Castro</name><affiliation wicri:level="1"><nlm:affiliation>Institute for Sustainable Agriculture (IAS), Spanish Council for Scientific Research (CSIC), Cordoba, Spain.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Institute for Sustainable Agriculture (IAS), Spanish Council for Scientific Research (CSIC), Cordoba</wicri:regionArea><wicri:noRegion>Cordoba</wicri:noRegion></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Agriculture (MeSH)</term><term>Automation (MeSH)</term><term>Crops, Agricultural (anatomy & histology)</term><term>Imaging, Three-Dimensional (MeSH)</term><term>Reference Standards (MeSH)</term><term>Remote Sensing Technology (MeSH)</term><term>Software (MeSH)</term><term>Time Factors (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Agriculture (MeSH)</term><term>Automatisation (MeSH)</term><term>Facteurs temps (MeSH)</term><term>Imagerie tridimensionnelle (MeSH)</term><term>Logiciel (MeSH)</term><term>Normes de référence (MeSH)</term><term>Produits agricoles (anatomie et histologie)</term><term>Technologie de télédétection (MeSH)</term></keywords><keywords scheme="MESH" qualifier="anatomie et histologie" xml:lang="fr"><term>Produits agricoles</term></keywords><keywords scheme="MESH" qualifier="anatomy & histology" xml:lang="en"><term>Crops, Agricultural</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Agriculture</term><term>Automation</term><term>Imaging, Three-Dimensional</term><term>Reference Standards</term><term>Remote Sensing Technology</term><term>Software</term><term>Time Factors</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Agriculture</term><term>Automatisation</term><term>Facteurs temps</term><term>Imagerie tridimensionnelle</term><term>Logiciel</term><term>Normes de référence</term><term>Technologie de télédétection</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A procedure to achieve the semi-automatic relative image normalization of multitemporal remote images of an agricultural scene called ARIN was developed using the following procedures: 1) defining the same parcel of selected vegetative pseudo-invariant features (VPIFs) in each multitemporal image; 2) extracting data concerning the VPIF spectral bands from each image; 3) calculating the correction factors (CFs) for each image band to fit each image band to the average value of the image series; and 4) obtaining the normalized images by linear transformation of each original image band through the corresponding CF. ARIN software was developed to semi-automatically perform the ARIN procedure. We have validated ARIN using seven GeoEye-1 satellite images taken over the same location in Southern Spain from early April to October 2010 at an interval of approximately 3 to 4 weeks. The following three VPIFs were chosen: citrus orchards (CIT), olive orchards (OLI) and poplar groves (POP). In the ARIN-normalized images, the range, standard deviation (s. d.) and root mean square error (RMSE) of the spectral bands and vegetation indices were considerably reduced compared to the original images, regardless of the VPIF or the combination of VPIFs selected for normalization, which demonstrates the method's efficacy. The correlation coefficients between the CFs among VPIFs for any spectral band (and all bands overall) were calculated to be at least 0.85 and were significant at P = 0.95, indicating that the normalization procedure was comparably performed regardless of the VPIF chosen. ARIN method was designed only for agricultural and forestry landscapes where VPIFs can be identified. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24604031</PMID><DateCompleted><Year>2015</Year><Month>02</Month><Day>21</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>9</Volume><Issue>3</Issue><PubDate><Year>2014</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS One</ISOAbbreviation></Journal><ArticleTitle>Semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features.</ArticleTitle><Pagination><MedlinePgn>e91275</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0091275</ELocationID><Abstract><AbstractText>A procedure to achieve the semi-automatic relative image normalization of multitemporal remote images of an agricultural scene called ARIN was developed using the following procedures: 1) defining the same parcel of selected vegetative pseudo-invariant features (VPIFs) in each multitemporal image; 2) extracting data concerning the VPIF spectral bands from each image; 3) calculating the correction factors (CFs) for each image band to fit each image band to the average value of the image series; and 4) obtaining the normalized images by linear transformation of each original image band through the corresponding CF. ARIN software was developed to semi-automatically perform the ARIN procedure. We have validated ARIN using seven GeoEye-1 satellite images taken over the same location in Southern Spain from early April to October 2010 at an interval of approximately 3 to 4 weeks. The following three VPIFs were chosen: citrus orchards (CIT), olive orchards (OLI) and poplar groves (POP). In the ARIN-normalized images, the range, standard deviation (s. d.) and root mean square error (RMSE) of the spectral bands and vegetation indices were considerably reduced compared to the original images, regardless of the VPIF or the combination of VPIFs selected for normalization, which demonstrates the method's efficacy. The correlation coefficients between the CFs among VPIFs for any spectral band (and all bands overall) were calculated to be at least 0.85 and were significant at P = 0.95, indicating that the normalization procedure was comparably performed regardless of the VPIF chosen. ARIN method was designed only for agricultural and forestry landscapes where VPIFs can be identified. </AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Garcia-Torres</LastName><ForeName>Luis</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Institute for Sustainable Agriculture (IAS), Spanish Council for Scientific Research (CSIC), Cordoba, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Caballero-Novella</LastName><ForeName>Juan J</ForeName><Initials>JJ</Initials><AffiliationInfo><Affiliation>Institute for Sustainable Agriculture (IAS), Spanish Council for Scientific Research (CSIC), Cordoba, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gómez-Candón</LastName><ForeName>David</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Institute for Sustainable Agriculture (IAS), Spanish Council for Scientific Research (CSIC), Cordoba, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>De-Castro</LastName><ForeName>Ana Isabel</ForeName><Initials>AI</Initials><AffiliationInfo><Affiliation>Institute for Sustainable Agriculture (IAS), Spanish Council for Scientific Research (CSIC), Cordoba, Spain.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>03</Month><Day>06</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000383" MajorTopicYN="Y">Agriculture</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001331" MajorTopicYN="N">Automation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018556" MajorTopicYN="N">Crops, Agricultural</DescriptorName><QualifierName UI="Q000033" MajorTopicYN="N">anatomy & histology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D021621" MajorTopicYN="Y">Imaging, Three-Dimensional</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012015" MajorTopicYN="N">Reference Standards</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058998" MajorTopicYN="Y">Remote Sensing Technology</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012984" MajorTopicYN="N">Software</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013997" MajorTopicYN="N">Time Factors</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2013</Year><Month>10</Month><Day>11</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2014</Year><Month>02</Month><Day>09</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>3</Month><Day>8</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>3</Month><Day>8</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>2</Month><Day>24</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">24604031</ArticleId><ArticleId IdType="doi">10.1371/journal.pone.0091275</ArticleId><ArticleId IdType="pii">PONE-D-13-42806</ArticleId><ArticleId IdType="pmc">PMC3946354</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>PLoS One. 2013;8(3):e58210</Citation><ArticleIdList><ArticleId IdType="pubmed">23483997</ArticleId></ArticleIdList></Reference><Reference><Citation>IEEE Trans Image Process. 2007 Feb;16(2):463-78</Citation><ArticleIdList><ArticleId IdType="pubmed">17269639</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Espagne</li></country></list><tree><country name="Espagne"><noRegion><name sortKey="Garcia Torres, Luis" sort="Garcia Torres, Luis" uniqKey="Garcia Torres L" first="Luis" last="Garcia-Torres">Luis Garcia-Torres</name></noRegion><name sortKey="Caballero Novella, Juan J" sort="Caballero Novella, Juan J" uniqKey="Caballero Novella J" first="Juan J" last="Caballero-Novella">Juan J. Caballero-Novella</name><name sortKey="De Castro, Ana Isabel" sort="De Castro, Ana Isabel" uniqKey="De Castro A" first="Ana Isabel" last="De-Castro">Ana Isabel De-Castro</name><name sortKey="G Mez Cand N, David" sort="G Mez Cand N, David" uniqKey="G Mez Cand N D" first="David" last="G Mez-Cand N">David G Mez-Cand N</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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