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Title (Primary) Supervised classification of agricultural land cover using a modified k-NN technique (MNN) and landsat remote sensing imagery
Author Samaniego, L.; Schulz, K.;
Journal Remote Sensing
Year 2009
Department CHS;
Volume 1
Issue 4
Language englisch;
Keywords Land use classification; supervised classification; nearest neighbors; agricultural land cover; crops
Abstract 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.
ID 554
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=554
Samaniego, L., Schulz, K. (2009):
Supervised classification of agricultural land cover using a modified k-NN technique (MNN) and landsat remote sensing imagery
Remote Sens. 1 (4), 875 - 895