Format:
Online-Ressource
Content:
Nearest neighbor 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 especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to find an adaptive metric that combines a local variance reducing technique and a linear embedding of the observation space into an appropriate Euclidean space. To illustrate the application of this technique, two agricultural land cover classifications using mono-temporal and multi-temporal Landsat scenes are presented. The results of the study, compared with standard approaches used in remote sensing such as maximum likelihood (ML) or k-Nearest Neighbor (k-NN) indicate substantial improvement with regard to the overall accuracy and the cardinality of the calibration data set. Also, using MNN in a soft/fuzzy classification framework demonstrated to be a very useful tool in order to derive critical areas that need some further attention and investment concerning additional calibration data.
In:
Datenlieferant: Open Access LMU (Ludwig-Maximilians-University Munich)
Language:
English
URN:
urn:nbn:de:bvb:19-epub-15991-3
URL:
https://doi.org/10.3390/rs1040875
URL:
https://nbn-resolving.org/urn:nbn:de:bvb:19-epub-15991-3
URL:
https://epub.ub.uni-muenchen.de/15991/1/oa_15991.htm
URL:
https://epub.ub.uni-muenchen.de/15991/