Name
Abstract | ||
|---|---|---|
Polarimetric synthetic aperture radar (PolSAR) is of great importance in the remote sensing, which can be used widely in both civil and military fields. However, existing classification methods cannot effectively utilize the spatial structure information of the data. In this study, a classification method for PolSAR images based on non-negative tensor factorization (NTF) is proposed. The proposed method uses tensor to represent the original data and extract the spacial structure feature by using NTF. Classification results are obtained by using support vector machines (SVM) and the Wishart clustering technology. The results show the validity and accuracy of the proposed method on PolSAR images classification. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7729291 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
PolSAR, NTF, tensor, SVM | Computer vision,Tensor,Pattern recognition,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Tensor factorization,Cluster analysis,Contextual image classification,Wishart distribution,Sparse matrix | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuiping Gou | 1 | 117 | 22.90 |
| Wenshuai Chen | 2 | 1 | 0.72 |
| Yizhou Liu | 3 | 0 | 0.34 |
| Pengcheng Li | 4 | 0 | 0.34 |
| Licheng Jiao | 5 | 5698 | 475.84 |