Name
Abstract | ||
|---|---|---|
This paper presents a multiple classifier scheme, known as Multiple Self-Organizing Maps (MSOM), for remote sensing classification problems. Based on the Kohonen SOM, multiple maps are fused, in either unsupervised, supervised or hybrid manners, so as to explore discrimination information from the data itself. The MSOM has the capability to extract and represent high-order statistics of high dimensional data from disparate sources in a nonparametric, vector-quantization fashion. The computation cost is linear in relation to the dimensionality and the operation complexity is simple and equivalent to a minimum-distance classifier. Thus, MSOM is very suitable for remote sensing applications under various data and design-sample conditions. We also demonstrate that the MSOM can be used for hyperspectral data clustering and joint spatio-temporal classification. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/3-540-45014-9_29 | Multiple Classifier Systems |
Keywords | Field | DocType |
multiple self-organizing maps,multiple classifier scheme,remote sensing classification,hyperspectral data,multiple self-organizing map scheme,kohonen som,classification problem,joint spatio-temporal classification,minimum-distance classifier,various data,high dimensional data,multiple map,remote sensing | Data mining,Clustering high-dimensional data,Computer science,Remote sensing,Hyperspectral imaging,Remote sensing application,Curse of dimensionality,Self-organizing map,Classifier (linguistics),Cluster analysis,Hybrid system | Conference |
Volume | ISSN | ISBN |
1857 | 0302-9743 | 3-540-67704-6 |
Citations | PageRank | References |
1 | 0.37 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weijian Wan | 1 | 11 | 2.48 |
| Donald Fraser | 2 | 78 | 8.29 |