Name
Abstract | ||
|---|---|---|
In this letter, we show how active learning can be particularly promising for classifying remote sensing images at large scales. The classification model constructed on samples extracted from a limited region of the image, called source domain, exhibits generally poor accuracies when used to predict the samples of a different region, called target domain, due to possible changes in class distribut... |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/LGRS.2013.2255258 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Remote sensing,Training,Adaptation models,MODIS,Space exploration,Support vector machines,Vegetation mapping | Feature detection (computer vision),Remote sensing,Artificial intelligence,Cluster analysis,Contextual image classification,Computer vision,Feature vector,Active learning,Pattern recognition,Feature (computer vision),Feature extraction,Initialization,Mathematics | Journal |
Volume | Issue | ISSN |
11 | 1 | 1545-598X |
Citations | PageRank | References |
6 | 0.46 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naif Alajlan | 1 | 839 | 50.51 |
| Edoardo Pasolli | 2 | 285 | 17.04 |
| Farid Melgani | 3 | 1100 | 80.98 |
| Andrea Franzoso | 4 | 6 | 0.46 |