Name
Title | ||
|---|---|---|
Color space transformation and object oriented based information extraction of aerial images |
Abstract | ||
|---|---|---|
Low-altitude aerial remote sensing platforms accessed reality multi-color images which had obvious characteristics and fitted for visual interpretation. These images were lacking of spectral information but rich in shape and texture information. But, the reality was that there was less study on the automatic extraction of ground information from aerial images. In this paper, UAV images were selected as test data. By combining the object oriented method and the multi-resolution segmentation, the paper selected some effective characteristics, constructed the rule sets and classify the image into water, shrub, farmland, road, and house. Then, the result was compared with which obtained by maximum likelihood classification method. The results showed that: With the object-oriented method, it could get higher accuracies and efficiencies for actual applications, the overall classification accuracies and Kappa coefficient are more than 85%. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/Geoinformatics.2013.6626201 | Geoinformatics |
Keywords | Field | DocType |
remote sensing,aerial images,object-oriented classification,spectral information,ihs transformation,visual interpretation,unmanned aerial vehicle (uav),unmanned aerial vehicle,automatic ground information extraction,object oriented method,shape information,image resolution,rule set,image segmentation,texture information,autonomous aerial vehicles,image classification,geophysical image processing,multiresolution segmentation,kappa coefficient,color space transformation,uav images,low-altitude aerial remote sensing platforms,multicolor images,image texture,rule sets,classification accuracies,multi-resolution segmentation,image colour analysis,object-oriented methods,shape,spatial resolution,data mining,feature extraction,accuracy | Scale-space segmentation,Image fusion,Computer science,Remote sensing,Image processing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Contextual image classification,Computer vision,Pattern recognition,Image texture,Image resolution | Conference |
Volume | Issue | ISSN |
null | null | 2161-024X |
Citations | PageRank | References |
1 | 0.36 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Xu | 1 | 1 | 0.36 |
| Fuzhou Duan | 2 | 7 | 3.60 |