An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Finding of Main Roads in Aerial Images by Using Geometric-Stochastic Models and Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Thin nets and crest lines: application to satellite data and medical images
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Learning to detect roads in high-resolution aerial images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
| Hi-index | 0.00 |
Humans can fairly easily identify roads in remote sensing images, but this has turned out to be a difficult task for computers. Most previous work in this area has utilized statistical and rule-based techniques, which depended primarily upon spectral information. However, it appears that spectral information alone is insufficient to identify roads in Landsat Thematic Mapper satellite imagery, since soils have the same spectral signature in the data as roads, and that contextual information is required. In this application, artificial neural networks are found to be superior to several previous techniques due in part to their ability to incorporate both spectral and contextual information. However, a number of factors cause problems for the network, and further work must be done to include additional information; it is suggested that a hybrid system might alleviate most of these difficulties.