Name
Abstract | ||
|---|---|---|
Semantic segmentation of aerial images refers to assigning one land cover category to each pixel. This is a challenging task due to the great differences in the appearances of ground objects. Many attempts have been made during the past decades. In recent years, convolutional neural networks (CNNs) have been introduced in the remote sensing field, and various solutions have been proposed to realiz... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LGRS.2017.2778181 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Semantics,Image segmentation,Training,Remote sensing,Labeling,Data models | Data modeling,Computer vision,Data set,Convolutional neural network,Segmentation,Image segmentation,Shuffling,Artificial intelligence,Pixel,Semantics,Mathematics | Journal |
Volume | Issue | ISSN |
15 | 2 | 1545-598X |
Citations | PageRank | References |
3 | 0.39 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaiqiang Chen | 1 | 4 | 1.45 |
| Kun Fu | 2 | 414 | 57.81 |
| Menglong Yan | 3 | 18 | 1.67 |
| Xin Gao | 4 | 4 | 2.09 |
| Xian Sun | 5 | 26 | 4.55 |
| Xin Wei | 6 | 4 | 1.78 |