Name
Title | ||
|---|---|---|
Classification of High-Resolution Remote-Sensing Image Using OpenStreetMap Information. |
Abstract | ||
|---|---|---|
Prior information about classes plays an important role in the high-resolution image classification. Produced by volunteers with GPS tracking practice and local knowledge, the crowdsourced OpenStreetMap (OSM) data have shown potential as a time-saving and cost-effective way to provide prior information for image classification. In this letter, we develop a high-resolution remote-sensing image clas... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LGRS.2017.2762466 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Training,Roads,Remote sensing,Redundancy,Global Positioning System,Image segmentation,Support vector machines | Training set,Superimposition,Remote sensing,Support vector machine,Image segmentation,Redundancy (engineering),Global Positioning System,Contextual image classification,Completeness (statistics),Mathematics | Journal |
Volume | Issue | ISSN |
14 | 12 | 1545-598X |
Citations | PageRank | References |
2 | 0.36 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taili Wan | 1 | 2 | 0.70 |
| Hongyang Lu | 2 | 9 | 2.47 |
| Qikai Lu | 3 | 37 | 3.92 |
| Nianxue Luo | 4 | 3 | 1.05 |