Automatic class labeling of classified imagery using a hyperspectral library

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dc.contributor.authorParshakov, Ilia
dc.contributor.supervisorStaenz, Karl
dc.contributor.supervisorCoburn, Craig A.
dc.date.accessioned2014-02-25T23:35:53Z
dc.date.available2014-02-25T23:35:53Z
dc.date.issued2012
dc.degree.levelMasters
dc.descriptionvii, 93 leaves : ill., maps (some col.) ; 29 cmen_US
dc.description.abstractImage classification is a fundamental information extraction procedure in remote sensing that is used in land-cover and land-use mapping. Despite being considered as a replacement for manual mapping, it still requires some degree of analyst intervention. This makes the process of image classification time consuming, subjective, and error prone. For example, in unsupervised classification, pixels are automatically grouped into classes, but the user has to manually label the classes as one land-cover type or another. As a general rule, the larger the number of classes, the more difficult it is to assign meaningful class labels. A fully automated post-classification procedure for class labeling was developed in an attempt to alleviate this problem. It labels spectral classes by matching their spectral characteristics with reference spectra. A Landsat TM image of an agricultural area was used for performance assessment. The algorithm was used to label a 20- and 100-class image generated by the ISODATA classifier. The 20-class image was used to compare the technique with the traditional manual labeling of classes, and the 100-class image was used to compare it with the Spectral Angle Mapper and Maximum Likelihood classifiers. The proposed technique produced a map that had an overall accuracy of 51%, outperforming the manual labeling (40% to 45% accuracy, depending on the analyst performing the labeling) and the Spectral Angle Mapper classifier (39%), but underperformed compared to the Maximum Likelihood technique (53% to 63%). The newly developed class-labeling algorithm provided better results for alfalfa, beans, corn, grass and sugar beet, whereas canola, corn, fallow, flax, potato, and wheat were identified with similar or lower accuracy, depending on the classifier it was compared with.en_US
dc.identifier.urihttps://hdl.handle.net/10133/3372
dc.language.isoen_CAen_US
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Geography, c2012en_US
dc.publisher.departmentDepartment of Geographyen_US
dc.publisher.facultyArts and Scienceen_US
dc.subjectRemote-sensing images -- Data processingen_US
dc.subjectImage processing -- Digital techniquesen_US
dc.subjectPattern recognition systemsen_US
dc.subjectAlgorithmsen_US
dc.subjectImage analysis -- Mathematicsen_US
dc.subjectImage analysis -- Data processingen_US
dc.subjectDissertations, Academicen_US
dc.titleAutomatic class labeling of classified imagery using a hyperspectral libraryen_US
dc.typeThesisen_US
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