Name
Title | ||
|---|---|---|
Adaptive parametric estimation and classification of remotely sensed imagery using a pyramid structure. |
Abstract | ||
|---|---|---|
An unsupervised region-based image segmentation algorithm implemented with a pyramid structure has been developed. Rather than depending on traditional local splitting and merging of regions with a similarity test of region statistics, the algorithm identifies the homogeneous and boundary regions at each level of pyramid, then the global parameters of each class are estimated and updated with values of the homogeneous regions represented at that level of the pyramid using mixture distribution estimation. The image is then classified through the pyramid structure. Classification results obtained for both simulated and SPOT imagery are presented. |
Year | DOI | Venue |
|---|---|---|
1991 | 10.1109/36.135810 | IEEE Trans. Geoscience and Remote Sensing |
Keywords | Field | DocType |
statistical distributions,spatial resolution,testing,em algorithm,image classification,mixture distribution,image segmentation,earth,photogrammetry,satellites,statistical analysis,helium,merging,remote sensing,parameter estimation | Mixture distribution,Remote sensing,Pyramid (image processing),Image segmentation,Artificial intelligence,Pyramid,Estimation theory,Contextual image classification,Computer vision,Photogrammetry,Pattern recognition,Image resolution,Mathematics | Journal |
Volume | Issue | ISSN |
29 | 4 | 0196-2892 |
Citations | PageRank | References |
2 | 1.30 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Kim | 1 | 2 | 1.30 |
| M. M. Crawford | 2 | 26 | 6.50 |