Name
Abstract | ||
|---|---|---|
Virtual reconstruction of large landscapes from satellite imagery can be a time-consuming task due to the number of objects that must be extracted. Poor image resolution and noise hinder automatic detection processes and thus must be corrected by the user. This paper describes an application that allows the user to guide the automatic detection of trees from satellite imagery and spatial vegetation data. The requirements of the system are specified and an architecture that satisfies these constraints is presented. The resulting application provides an intuitive computer-aided method for the selection and classification of trees. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.3233/JCM-2010-0264 | Journal of Computational Methods in Sciences and Engineering - Special Supplement Issue in Section A and B: Selected Papers from the ISCA International Conference on Software Engineering and Data Engineering, 2009 |
Keywords | DocType | Volume |
spatial vegetation data,automatic detection,virtual reconstruction,time-consuming task,large landscape,poor image resolution,automatic detection process,tree detection,intuitive computer-aided method,resulting application,satellite imagery,image processing,virtual reality | Conference | 10 |
Issue | ISSN | Citations |
1-2S1 | 1472-7978 | 0 |
PageRank | References | Authors |
0.34 | 2 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David T. Brown | 1 | 65 | 194.74 |
| Roger V. Hoang | 2 | 32 | 5.79 |
| Matthew R. Sgambati | 3 | 2 | 2.38 |
| Timothy J. Brown | 4 | 17 | 3.74 |
| Sergiu Dascalu | 5 | 362 | 79.10 |
| Frederick C. Harris Jr. | 6 | 547 | 78.86 |