Name
Abstract | ||
|---|---|---|
This paper presents a statistical method for deriving the optimal prospective field sampling scheme on a remote sensing image to represent different categories in the field. The iterated conditional modes algorithm (ICM) is used for segmentation followed by simulated annealing within each category. Derived field sampling points are more intense in heterogenous segments. This method is applied to airborne hyperspectral data from an agricultural field. The optimized sampling scheme shows superiority to simple random sampling and rectangular grid sampling in estimating common vegetation indices and is thus more representative of the whole study area. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-69839-5_55 | ICCSA (1) |
Keywords | Field | DocType |
common vegetation index,segmented image,field sampling,airborne hyperspectral data,simple random sampling,optimized sampling scheme,optimal prospective field,agricultural field,statistical method,heterogenous segment,different category,derived field,simulated annealing | Simple random sample,Computer science,Image segmentation,Artificial intelligence,Slice sampling,Simulated annealing,Computer vision,Mathematical optimization,Pattern recognition,Segmentation,Hyperspectral imaging,Sampling (statistics),Iterated conditional modes | Conference |
Volume | ISSN | Citations |
5072 | 0302-9743 | 1 |
PageRank | References | Authors |
0.45 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pravesh Debba | 1 | 58 | 7.44 |
| Alfred Stein | 2 | 30 | 4.85 |
| Freek van der Meer | 3 | 313 | 43.05 |
| Emmanuel John M. Carranza | 4 | 237 | 21.82 |
| Arko Lucieer | 5 | 455 | 46.51 |