Name
Title | ||
|---|---|---|
Self-paced stacked denoising autoencoders based on differential evolution for change detection. |
Abstract | ||
|---|---|---|
•We put forward a self-paced stacked denoising autoencoders model for change detection in radar images.•Every training sample is assigned with a weight, then deep network stacked denoising autoencoders is adopted to learn these weighted samples.•Self-paced learning is employed for alternately training stacked denoising autoencoders and updating the sample weights.•We adopt differential evolution to optimize the pace parameter used in the proposed model. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.asoc.2018.07.021 | Applied Soft Computing |
Keywords | Field | DocType |
Image change detection,Synthetic aperture radar,Self-paced learning,Stacked denoising autoencoders,Differential evolution | Noise reduction,Change detection,Synthetic aperture radar image,Pattern recognition,Synthetic aperture radar,Differential evolution,Artificial intelligence,Speckle noise,Classifier (linguistics),Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
71 | 1568-4946 | 1 |
PageRank | References | Authors |
0.38 | 34 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Li | 1 | 143 | 10.82 |
| Maoguo Gong | 2 | 2676 | 172.02 |
| Congcong Wang | 3 | 6 | 3.82 |
| Qiguang Miao | 4 | 355 | 49.69 |