Name
Abstract | ||
|---|---|---|
In this paper, the authors describe and implement an algorithm to perform a supervised classification into Landsat MSS satellite images. The Maximum Likelihood Classification method is used to generate raster digital thematic maps by means of a supervised clustering. The clustering method has been proved in Landsat MSS images of different regions of Mexico to detect several training data related to the geographic environment. The algorithm has been integrated into Spatial Analyzer Module to improve the decision making model and the spatial analysis into GIS-applications. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.4018/ijksr.2013070110 | International Journal of Knowledge Society Research |
Keywords | DocType | Volume |
supervised classification,landsat mss satellite image,landsat mss image,different region,supervised clustering,clustering method,landsat mss images,raster digital thematic map,maximum likelihood classification method,spatial analyzer module,geographic environment | Journal | 4 |
Issue | Citations | PageRank |
3 | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miguel Torres | 1 | 43 | 12.31 |
| Marco Moreno-Ibarra | 2 | 33 | 6.84 |
| Rolando Quintero | 3 | 59 | 16.08 |
| Giovanni Guzmán | 4 | 51 | 13.61 |