Name
Title | ||
|---|---|---|
Deep Learning and Superpixel Feature Extraction Based on Contractive Autoencoder for Change Detection in SAR Images. |
Abstract | ||
|---|---|---|
Image segmentation based on superpixel is used in urban and land cover change detection for fast locating region of interest. However, the segmentation algorithms often degrade due to speckle noise in synthetic aperture radar images. In this paper, a feature learning method using a stacked contractive autoencoder (sCAE) is presented to extract the temporal change feature from superpixel with noise... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TII.2018.2873492 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Feature extraction,Image segmentation,Change detection algorithms,Synthetic aperture radar,Clustering algorithms | Autoencoder,Change detection,Pattern recognition,Synthetic aperture radar,Computer science,Real-time computing,Image segmentation,Feature extraction,Artificial intelligence,Speckle noise,Cluster analysis,Feature learning | Journal |
Volume | Issue | ISSN |
14 | 12 | 1551-3203 |
Citations | PageRank | References |
9 | 0.55 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Lv | 1 | 31 | 11.32 |
| Chen Chen | 2 | 145 | 21.68 |
| Tie Qiu | 3 | 895 | 80.18 |
| Arun Kumar | 4 | 1427 | 132.32 |