Name
Title | ||
|---|---|---|
Dehazing for Multispectral Remote Sensing Images Based on a Convolutional Neural Network With the Residual Architecture. |
Abstract | ||
|---|---|---|
Multispectral remote sensing images are often contaminated by haze, which causes low image quality. In this paper, a novel dehazing method based on a deep convolutional neural network (CNN) with the residual structure is proposed for multispectral remote sensing images. First, multiple CNN individuals with the residual structure are connected in parallel and each individual is used to learn a regr... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/JSTARS.2018.2812726 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Keywords | Field | DocType |
Remote sensing,Atmospheric modeling,Earth,Atmospheric waves,Scattering,Cloud computing,Image restoration | Computer vision,Residual,Convolution,Convolutional neural network,Multispectral image,Image quality,Artificial intelligence,Multispectral pattern recognition,Image restoration,Mathematics,Haze | Journal |
Volume | Issue | ISSN |
11 | 5 | 1939-1404 |
Citations | PageRank | References |
3 | 0.38 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manjun Qin | 1 | 3 | 0.38 |
| Fengying Xie | 2 | 182 | 15.33 |
| Wei Li | 3 | 436 | 140.67 |
| Zhenwei Shi | 4 | 559 | 63.11 |
| Haopeng Zhang | 5 | 47 | 14.75 |