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Article: Forest cover classification by optimal segmentation of high resolution satellite imagery

TitleForest cover classification by optimal segmentation of high resolution satellite imagery
Authors
KeywordsPixel-based classification
High resolution
Digital forest cover map
Satellite image
Segment-based classification
Issue Date2011
Citation
Sensors, 2011, v. 11, n. 2, p. 1943-1958 How to Cite?
AbstractThis study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens® Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the "salt-and-pepper effect" and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images.
Persistent Identifierhttp://hdl.handle.net/10722/296476
ISSN
2023 Impact Factor: 3.4
2023 SCImago Journal Rankings: 0.786
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorKim, So Ra-
dc.contributor.authorLee, Woo Kyun-
dc.contributor.authorKwak, Doo Ahn-
dc.contributor.authorBiging, Greg S.-
dc.contributor.authorGong, Peng-
dc.contributor.authorLee, Jun Hak-
dc.contributor.authorCho, Hyun Kook-
dc.date.accessioned2021-02-25T15:15:59Z-
dc.date.available2021-02-25T15:15:59Z-
dc.date.issued2011-
dc.identifier.citationSensors, 2011, v. 11, n. 2, p. 1943-1958-
dc.identifier.issn1424-8220-
dc.identifier.urihttp://hdl.handle.net/10722/296476-
dc.description.abstractThis study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens® Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the "salt-and-pepper effect" and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images.-
dc.languageeng-
dc.relation.ispartofSensors-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectPixel-based classification-
dc.subjectHigh resolution-
dc.subjectDigital forest cover map-
dc.subjectSatellite image-
dc.subjectSegment-based classification-
dc.titleForest cover classification by optimal segmentation of high resolution satellite imagery-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3390/s110201943-
dc.identifier.pmid22319391-
dc.identifier.pmcidPMC3274007-
dc.identifier.scopuseid_2-s2.0-79952074186-
dc.identifier.volume11-
dc.identifier.issue2-
dc.identifier.spage1943-
dc.identifier.epage1958-
dc.identifier.isiWOS:000287735400043-
dc.identifier.issnl1424-8220-

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