Unsupervised clustering of spectral signatures in Landsat imagery : a thesis presented in partial fulfilment of the requirements for the degree of Master of Arts in Computer Science at Massey University
dc.contributor.author | Clement, Brian Roy | |
dc.date.accessioned | 2017-06-22T20:32:14Z | |
dc.date.available | 2017-06-22T20:32:14Z | |
dc.date.issued | 1977 | |
dc.description.abstract | This thesis describes an investigation into automatic recognition of satellite imagery from the LANDSAT Project. Clustering techniques are shown to be the most suitable; of the three clustering algorithms investigated the k-means is shown to be the most effective. The need to perform edge detection on the images prior to clustering is also demonstrated. A suitable algorithm for edge detection is described. Indexing terms: clustering, LANDSAT Satellite project, pattern recognition, Satellite data | en_US |
dc.identifier.uri | http://hdl.handle.net/10179/11285 | |
dc.language.iso | en | en_US |
dc.publisher | Massey University | en_US |
dc.rights | The Author | en_US |
dc.subject | Landsat satellites | en_US |
dc.subject | Remote sensing | en_US |
dc.subject | Data processing | en_US |
dc.subject | Observations | en_US |
dc.title | Unsupervised clustering of spectral signatures in Landsat imagery : a thesis presented in partial fulfilment of the requirements for the degree of Master of Arts in Computer Science at Massey University | en_US |
dc.type | Thesis | en_US |
massey.contributor.author | Clement, Brian Roy | en_US |
thesis.degree.discipline | Computer Science | en_US |
thesis.degree.grantor | Massey University | en_US |
thesis.degree.level | Masters | en_US |
thesis.degree.name | Master of Arts (M. A.) | en_US |
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