| Title | Clustering Methods for Unsupervised Classification |
| Download | Downloads (Preprint) |
| |
| Licence | Please note the adoption of the Open Government Licence - Canada
supersedes any previous licences. |
| Author | Latifovic, R; Cihlar, J; Beaubien, J |
| Source | Fourth International Airborne Remote Sensing Conference and Exhibition/21st Canadian symposium on Remote Sensing, Ottawa, Ontario, Canada, 21-24 June; 1999., https://doi.org/10.4095/219517  Open Access |
| Year | 1999 |
| Alt Series | Earth Sciences Sector, Contribution Series 20042715 |
| Document | book |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Released | 1999 01 01 |
| Abstract | In this paper, we have examined characteristics of spectral clusters produced by several unsupervised classification algorithms. We have also designed a new cluster merging strategy for a previously
developed unsupervised classification procedure. The clustering methods were compared using a summer Landsat Thematic mapper image from central Canada which contained forest, cropland, wetland and other cover types. We have found that a strategy
employing spectral similarity and cluster size to guide the cluster merging process yields clusters in which spectral homogeneity and size are well balanced across the range of spectral clusters in which spectral homogeneity and size are well
balanced across the range of spectral clusters found in the scene. The new clustering approach requires only three control parameters, thus facilitating consistent results when applied in other areas. The results also suggest that the dual cluster
size - spectral homogeneity approach produces more consistent and refined clustering results than purely statistically-based methods, although testing over a broader range of conditions is needed to ascertain this with confidence. |
| GEOSCAN ID | 219517 |
|
|