Name
Abstract | ||
|---|---|---|
Abstract: A novel approach to the automatic classification of remote sensed images is proposed. This approach is based on a three-phase procedure: first pixels which belong to the areas of interest with large likelihood are selected as seeds; second the seeds are refined into connected shapes using two well known image processing techniques; third the results of the shape refinement algorithms are merged together. The initial seed extraction is performed using a simple thresholding strategy applied to NDVI4-3 index. Subsequently shape refinement through Seeded Region Growing and Watershed Decomposition is applied, finally a merging procedure is applied to build likelihood maps. Experimental results are presented to analyze the correctness and robustness of the method in recognizing vegetation areas around Mount Etna. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/ICIAP.2001.956998 | ICIAP |
Keywords | Field | DocType |
classifica- tion.,watershed decomposition,mount etna,connected shape,large likelihood,novel approach,shape refinement algorithm,seeded region,image processing,shape refinement,likelihood map,remote sensed images segmentation,remote sensing,three-phase procedure,automatic classification,image segmentation,indexation,image classification,maximum likelihood estimation,pixel,robustness,data mining,satellites,feature extraction,shape,merging | Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Image processing,Feature extraction,Image segmentation,Pixel,Region growing,Artificial intelligence,Thresholding,Contextual image classification | Conference |
ISBN | Citations | PageRank |
0-7695-1183-X | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. Gallo | 1 | 223 | 20.06 |
| g grasso | 2 | 0 | 0.34 |
| Salvatore Nicotra | 3 | 8 | 4.84 |
| Alfredo Pulvirenti | 4 | 388 | 23.92 |