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 |
Files
License bundle
1 - 1 of 1
Loading...
- Name:
- license.txt
- Size:
- 3.32 KB
- Format:
- Item-specific license agreed upon to submission
- Description: