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. ORCID logo ; 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
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