Name
Title | ||
|---|---|---|
Fusion of HJ1B and ALOS PALSAR data for land cover classification using machine learning methods. |
Abstract | ||
|---|---|---|
•Two methods were compared for classifying HJ1B data and PALSAR fused-images.•The overall accuracies of two methods were relatively close using SVM classifier.•The pixel-based classification performed relatively better than the object-based.•The overall accuracy can be improved by 5.7% using the proposed algorithms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.jag.2016.06.014 | International Journal of Applied Earth Observation and Geoinformation |
Keywords | Field | DocType |
HJ1B,ALOS/PALSAR,Image fusion,Land cover classification | Image fusion,Synthetic aperture radar,Remote sensing,Artificial intelligence,Classifier (linguistics),Contextual image classification,Random forest,Geography,Computer vision,Multispectral image,Support vector machine,Pixel,Machine learning | Journal |
Volume | ISSN | Citations |
52 | 0303-2434 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| X. Y. Wang | 1 | 1 | 0.35 |
| Y. Guo | 2 | 66 | 18.37 |
| Jiping He | 3 | 110 | 17.46 |
| L. T. Du | 4 | 1 | 0.35 |