Name
Title | ||
|---|---|---|
Using existing large-area land-cover maps to classify spatially high resolution images |
Abstract | ||
|---|---|---|
This paper presents Template-Guided Classification (TGC), a technique for using the class labels of existing large-area land-cover maps to automatically classify spatially highresolution images. TGC uses land-cover images as templates to guide hierarchical clustering and labeling. To test TGC, 10-m SPOT 5 HRG images and 1-m colour orthophotos of the Vermilion River watershed, Canada were classified into forest/non-forest classes using the 25-m Earth Observation for the Sustainable Development of forests (EOSD) landcover map as a template. Although the average accuracies of the 10-m SPOT classifications were poor, the 1-m orthophoto accuracies were much higher (87% forest user's accuracy, 82% forest producers accuracy, 93% overall accuracy). TGC classification accuracies were highly variable. Further investigation is needed to determine whether TGC can be made into a robust procedure. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IGARSS.2014.6947545 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
geophysical image processing,image classification,land cover,vegetation mapping,Canada,Vermilion River watershed,hierarchical clustering,large-area land-cover maps,spatially high-resolution image classification,template-guided classification,automatic classification,downscaling,hierarchical clustering,land-cover map reuse | Hierarchical clustering,Computer science,Remote sensing,Watershed,Earth observation,Land cover,Orthophoto | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Kennedy | 1 | 0 | 0.34 |
| Jinkai Zhang | 2 | 94 | 8.56 |
| Karl Staenz | 3 | 115 | 23.05 |
| Craig A. Coburn | 4 | 1 | 2.17 |