Applicability of Multi-date Land Cover Mapping using Landsat-5 TM Imagery in the Northeastern US
In many situations, multi-date image classification improves classification accuracies. However, with improved accuracies comes increased image processing time and effort. This work investigates the circumstances under which multi-date image classification is significantly better than single-date cl...
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| Veröffentlicht in: | Photogrammetric engineering and remote sensing 2013-04, Vol.79 (4), p.359-368 |
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| container_title | Photogrammetric engineering and remote sensing |
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| creator | MacLean, Meghan Graham Congalton, Russell G. |
| description | In many situations, multi-date image classification improves classification accuracies. However, with improved accuracies comes increased image processing time and effort. This work investigates the circumstances under which multi-date image classification is significantly better
than single-date classification using Landsat-5 TM imagery for southeastern New Hampshire. Multiple Landsat images were processed for every three years from 1986 to 2010 and classified using an object-based image analysis approach (OBIA) with a classification and regression tree (CART) technique.
Two maps were created for each of the mapping years, one using a single image, and another using multiple images from that year. The multi-date classification process generally performed better than the single-date process. However, the significance of the improvement was primarily dependent
on the accuracy of the single-date map. Therefore, if the accuracy of the singledate classification is acceptable, it may not be necessary to perform the multi-date classification. |
| doi_str_mv | 10.14358/PERS.79.4.359 |
| format | Article |
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Two maps were created for each of the mapping years, one using a single image, and another using multiple images from that year. The multi-date classification process generally performed better than the single-date process. However, the significance of the improvement was primarily dependent
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than single-date classification using Landsat-5 TM imagery for southeastern New Hampshire. Multiple Landsat images were processed for every three years from 1986 to 2010 and classified using an object-based image analysis approach (OBIA) with a classification and regression tree (CART) technique.
Two maps were created for each of the mapping years, one using a single image, and another using multiple images from that year. The multi-date classification process generally performed better than the single-date process. However, the significance of the improvement was primarily dependent
on the accuracy of the single-date map. Therefore, if the accuracy of the singledate classification is acceptable, it may not be necessary to perform the multi-date classification.</description><subject>Acceptability</subject><subject>Animal, plant and microbial ecology</subject><subject>Applied geophysics</subject><subject>Biological and medical sciences</subject><subject>Classification</subject><subject>Earth sciences</subject><subject>Earth, ocean, space</subject><subject>Exact sciences and technology</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects. Techniques</subject><subject>Image classification</subject><subject>Imagery</subject><subject>Internal geophysics</subject><subject>Land cover</subject><subject>Mapping</subject><subject>Regression</subject><subject>Teledetection and vegetation maps</subject><issn>0099-1112</issn><issn>2374-8079</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNqFkc1v1DAQxS0EEkvhytkXJC5J_ZXYPpZVgUq7FNH2bKaOvbjKJsF2Ki1_fZ3uihvCh5mR_N7P1huE3lNSU8Ebdf798sdNLXUtat7oF2jFuBSVIlK_RCtCtK4opew1epPSAyGcNlSu0M-LaeqDhfvQh3zAo8fbuc-h6iA7vIGhw-vx0UW8hWkKww7PaanLRYJcNfh2i6_2sHPxgMOA8y-Hv42xNEjZxQHf3bxFrzz0yb079TN09_nydv212lx_uVpfbCrbkDZXljCQ4KAjQrfOMWtdx6T3XnvhO9XoTsqOeustUVoxUK3Q3PpGdcXX2nt-hj4euVMcf88uZbMPybq-h8GNczJUtoxySVrxfylnqmFUNbRI66PUxjGl6LyZYthDPBhKzHPqZkndSG2EKakXw4cTG5KF3kcYbEh_XUzSlim66D4ddSVON2QwD-MchxKQgTTFYnClMEK5Ic-nPHAciDAQ8zLwArn-ByTYE2fZ-7J28yj1IAqRUaLY8ndKTec8lG2bDNHs_pjE-RNrcLHv</recordid><startdate>20130401</startdate><enddate>20130401</enddate><creator>MacLean, Meghan Graham</creator><creator>Congalton, Russell G.</creator><general>American Society for Photogrammetry and Remote Sensing</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TG</scope><scope>7U6</scope><scope>C1K</scope><scope>KL.</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>20130401</creationdate><title>Applicability of Multi-date Land Cover Mapping using Landsat-5 TM Imagery in the Northeastern US</title><author>MacLean, Meghan Graham ; Congalton, Russell G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c506t-c02a7aead0496ee2cced27fff9f4fd859d77d1fcfc08982a86493cf58d2a76cb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Acceptability</topic><topic>Animal, plant and microbial ecology</topic><topic>Applied geophysics</topic><topic>Biological and medical sciences</topic><topic>Classification</topic><topic>Earth sciences</topic><topic>Earth, ocean, space</topic><topic>Exact sciences and technology</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects. Techniques</topic><topic>Image classification</topic><topic>Imagery</topic><topic>Internal geophysics</topic><topic>Land cover</topic><topic>Mapping</topic><topic>Regression</topic><topic>Teledetection and vegetation maps</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>MacLean, Meghan Graham</creatorcontrib><creatorcontrib>Congalton, Russell G.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Photogrammetric engineering and remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>MacLean, Meghan Graham</au><au>Congalton, Russell G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Applicability of Multi-date Land Cover Mapping using Landsat-5 TM Imagery in the Northeastern US</atitle><jtitle>Photogrammetric engineering and remote sensing</jtitle><date>2013-04-01</date><risdate>2013</risdate><volume>79</volume><issue>4</issue><spage>359</spage><epage>368</epage><pages>359-368</pages><issn>0099-1112</issn><eissn>2374-8079</eissn><coden>PERSDV</coden><abstract>In many situations, multi-date image classification improves classification accuracies. However, with improved accuracies comes increased image processing time and effort. This work investigates the circumstances under which multi-date image classification is significantly better
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Two maps were created for each of the mapping years, one using a single image, and another using multiple images from that year. The multi-date classification process generally performed better than the single-date process. However, the significance of the improvement was primarily dependent
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| ispartof | Photogrammetric engineering and remote sensing, 2013-04, Vol.79 (4), p.359-368 |
| issn | 0099-1112 2374-8079 |
| language | eng |
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| source | Ingenta Connect; EZB Electronic Journals Library |
| subjects | Acceptability Animal, plant and microbial ecology Applied geophysics Biological and medical sciences Classification Earth sciences Earth, ocean, space Exact sciences and technology Fundamental and applied biological sciences. Psychology General aspects. Techniques Image classification Imagery Internal geophysics Land cover Mapping Regression Teledetection and vegetation maps |
| title | Applicability of Multi-date Land Cover Mapping using Landsat-5 TM Imagery in the Northeastern US |
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