Name
Title | ||
|---|---|---|
Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images |
Abstract | ||
|---|---|---|
Synthetic aperture radar (SAR) image segmentation is fundamental for the interpretation and understanding of these images. In this process, the representation of SAR image features plays an important role. Spectral clustering is an image segmentation method making it possible to combine features and cues. This study presents a new spectral clustering method using unsupervised feature learning (UFL... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1049/iet-cvi.2014.0295 | IET Computer Vision |
Keywords | Field | DocType |
feature extraction,image coding,image segmentation,learning (artificial intelligence),matrix decomposition,radar computing,radar imaging,synthetic aperture radar | Computer vision,Spectral clustering,Scale-space segmentation,Pattern recognition,Synthetic aperture radar,Feature (computer vision),Segmentation-based object categorization,Feature extraction,Image segmentation,Artificial intelligence,Mathematics,Feature learning | Journal |
Volume | Issue | ISSN |
9 | 5 | 1751-9632 |
Citations | PageRank | References |
9 | 0.57 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| masoumeh rahmani | 1 | 9 | 0.57 |
| Gholamreza Akbarizadeh | 2 | 51 | 6.19 |