Name
Abstract | ||
|---|---|---|
We propose an algorithm for very high-resolution satellite image classification that combines non-supervised segmentation with a supervised classification. Both multi-spectral data and local spatial priors are used in the Gaussian Hidden Markov Random Field (GHMRF) model for the segmentation. Then, two classifiers, Mahalanobis distance classifier and SVM, are studied using intensity, texture and shape features. Validation is done qualitatively and quantitatively by comparison with a manual classification used as a ground truth. Results show very good performance of our approach in comparison to existing techniques. Also, we demonstrate that spectral and spatial features calculated on segmented regions are much more discriminant than the spectral features of the pixels taken individually for the classification task. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICIP.2005.1530521 | 2005 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), VOLS 1-5 |
Keywords | Field | DocType |
gaussian processes,hidden markov models,image classification,support vector machines,mahalanobis distance,image resolution,classification,image segmentation,segmentation,ground truth,image texture | Computer vision,Pattern recognition,Hidden Markov random field,Computer science,Image texture,Support vector machine,Mahalanobis distance,Image segmentation,Ground truth,Artificial intelligence,Contextual image classification,Hidden Markov model | Conference |
ISSN | Citations | PageRank |
1522-4880 | 8 | 0.73 |
References | Authors | |
4 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xavier Gigandet | 1 | 8 | 0.73 |
| Meritxell Bach Cuadra | 2 | 326 | 23.59 |
| Abram Pointet | 3 | 8 | 1.07 |
| Leila Cammoun | 4 | 150 | 11.65 |
| Regis Caloz | 5 | 8 | 0.73 |
| Jean-Philippe Thiran | 6 | 2320 | 257.56 |