Name
Abstract | ||
|---|---|---|
Supervised learning is vital to classify impervious surface from satellite images. Despite its effectiveness, the training samples need to be provided manually, which is time consuming and labor intensive, or even impractical when classifying satellite images at the regional/global scale. This study, therefore, sets out to automatically generate training samples from open data, based on the fact t... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JSTARS.2019.2903585 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Keywords | Field | DocType |
Satellites,Remote sensing,Social networking (online),Surface treatment,Training,Urban areas,Earth | Impervious surface,Computer vision,Open data,Satellite,One-class classification,Filter (signal processing),Supervised learning,Artificial intelligence,Operational land imager,Mathematics,Satellite image | Journal |
Volume | Issue | ISSN |
12 | 4 | 1939-1404 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zelang Miao | 1 | 125 | 13.82 |
| Yuelong Xiao | 2 | 0 | 0.34 |
| Wenzhong Shi | 3 | 778 | 86.23 |
| Yueguang He | 4 | 0 | 0.34 |
| Paolo Gamba | 5 | 8 | 3.14 |
| Zhongbin Li | 6 | 44 | 6.86 |
| Alim Samat | 7 | 65 | 10.00 |
| Lixin Wu | 8 | 94 | 35.60 |
| Jia Li | 9 | 0 | 0.68 |
| Hao Wu | 10 | 9 | 1.66 |