Name
Title | ||
|---|---|---|
Unmixing polarimetric radar images based on land cover type before target decomposition |
Abstract | ||
|---|---|---|
A new method for unmixing radar polarimetric images with optical images is proposed. It was found that the polarimetric covariance matrix can be unmixed considering a linear model. As a result, this model is used to produce unmixed covariance matrices based on land cover types. We hope to prove that this unmixing of the polarimetric information produce greater information for land cover classification. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IGARSS.2014.6947055 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
covariance matrices,image classification,land cover,optical images,radar imaging,radar polarimetry,remote sensing by radar,terrain mapping,land cover classification,land cover types,linear model,optical images,polarimetric covariance matrix,polarimetric information,radar polarimetric image unmixing,target decomposition,unmixed covariance matrices,land cover,polarimetry,radar,unmixing | Radar,Computer vision,Radar imaging,Polarimetry,Linear model,Matrix (mathematics),Computer science,Remote sensing,Artificial intelligence,Covariance matrix,Land cover,Covariance | Conference |
ISSN | Citations | PageRank |
2153-6996 | 1 | 0.43 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastien Giordano | 1 | 1 | 1.45 |
| Grégoire Mercier | 2 | 605 | 52.49 |
| Jean-Paul Rudant | 3 | 90 | 12.90 |