Name
Abstract | ||
|---|---|---|
This paper proposes a novel method for classification of Remote Sensing images. In this method, the popular Maximum Likelihood Classifier (MLC) combined with the Support Vector Machine (SVM) classifier. This method computes the energy function of Markov Random Field (MRF) in the neighborhoods of the test pixels. Then, relates the Markovian energy-difference function to the SVM classifier. Therefore, the salt-and-pepper effect on the classified map is reduced using the proposed contextual classifier. In this paper, two datasets include a hyperspectral and a multispectral image are used. In order to evaluate the proposed method, classification results of this method are compared with MLC and SVM. Experimental results demonstrate that the proposed classification system significantly outperforms other approaches for both hyperspectral and multispectral images. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | Support Vector Machine (SVM), contextual image classification, Markov Random Field (MRF), a Maximum Posterior (MAP) |
Field | DocType | ISSN |
Structured support vector machine,Markov random field,Computer science,Remote sensing,Artificial intelligence,Classifier (linguistics),Computer vision,Pattern recognition,Multispectral image,Support vector machine,Hyperspectral imaging,Margin classifier,Quadratic classifier | Conference | 2153-6996 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Masjedi | 1 | 10 | 2.53 |
| Yasser Maghsoudi | 2 | 64 | 10.26 |
| Mohammad Javad Valadan Zoej | 3 | 65 | 10.19 |