Name
Title | ||
|---|---|---|
Decomposition-Feature-Iterative-Clustering-Based Superpixel Segmentation for PolSAR Image Classification. |
Abstract | ||
|---|---|---|
Compared with traditional pixel-based polarimetric synthetic aperture radar (PolSAR) image classification methods, superpixel-based methods take advantages of the spatial information of pixels, so they can overcome the influence of speckle noise on the classification result. Since traditional superpixel methods do not utilize the scattering characteristics of a PolSAR image, the boundaries of the ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LGRS.2018.2833492 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Image segmentation,Speckle,Adaptive optics,Optical imaging,Optical scattering,Classification algorithms | Spatial analysis,Computer vision,Speckle pattern,Image segmentation,Pixel,Artificial intelligence,Speckle noise,Contextual image classification,Cluster analysis,Statistical classification,Mathematics | Journal |
Volume | Issue | ISSN |
15 | 8 | 1545-598X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Biao Hou | 1 | 368 | 49.04 |
| Chen Yang | 2 | 10 | 2.96 |
| Bo Ren | 3 | 72 | 12.60 |
| Licheng Jiao | 4 | 5698 | 475.84 |