Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1109/tgrs.2008.916629 |
| Titel (primär) | Supervised classification of remotely sensed imagery using a modified k-NN technique |
| Autor | Samaniego, L.
; Bárdossy, A.; Schulz, K. |
| Quelle | IEEE Transactions on Geoscience and Remote Sensing |
| Erscheinungsjahr | 2008 |
| Department | CLE; CHS |
| Band/Volume | 46 |
| Heft | 7 |
| Seite von | 2112 |
| Seite bis | 2125 |
| Sprache | englisch |
| Keywords | dimensionality reduction; ensemble prediction; k-nearest neighbors (NNs); land cover classification; simulated annealing (SA) |
| Abstract | Nearest neighbor (NN) techniques are commonly used in remote sensing, pattern recognition, and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are particularly useful in those cases exhibiting a highly nonlinear relationship between variables. In most studies, the distance measure is adopted a priori. In contrast, we propose a general procedure to find Euclidean metrics in a low-dimensional space (i.e., one in which the number of dimensions is less than the number of predictor variables) whose main characteristic is to minimize the variance of a given class label of all those pairs of points whose distance is less than a predefined value. $k$-NN is used in each embedded space to determine the possibility that a query belongs to a given class label. The class estimation is carried out by an ensemble of predictions. To illustrate the application of this technique, a typical land cover classification using a Landsat-5 Thematic Mapper scene is presented. Experimental results indicate substantial improvement with regard to the classification accuracy as compared with approaches such as maximum likelihood, linear discriminant analysis, standard $k$-NN, and adaptive quasi-conformal kernel $k$ -NN. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=1390 |
| Samaniego, L., Bárdossy, A., Schulz, K. (2008): Supervised classification of remotely sensed imagery using a modified k-NN technique IEEE Trans. Geosci. Remote Sensing 46 (7), 2112 - 2125 10.1109/tgrs.2008.916629 |
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