On the nature of models in remote sensing
Remote Sensing of Environment
Machine vision
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
| Hi-index | 0.00 |
An object-based approach was utilized in the development of two urban land-cover classification schemes on high-resolution (0.6 m), true-colour aerial photography of the Phoenix metropolitan area, USA. An initial classification scheme was heavily weighted by standard nearest-neighbour (SNN) functions generated by samples from each of the classes, which produced an enhanced accuracy (84%). A second classification was developed from the initial classification scheme in which SNN functions were transformed into a fuzzy-rule set, creating a product transportable to different areas of the same imagery, or for land-cover change detection with similar imagery. A comprehensive accuracy assessment revealed a slightly lower overall accuracy (79%) for the rule-based classification. We conclude that the transportable classification scheme is satisfactory for general land-cover analyses; yet classification accuracy can be enhanced at site-specific venues with the incorporation of nearest-neighbour functions using class samples.