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Remote Sens. 2009, 1(4), 875-895; doi:10.3390/rs1040875

Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN) and Landsat Remote Sensing Imagery

1
Department of Computational Hydrosystems, UFZ–Helmholtz-Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany
2
Department of Geography, Ludwig-Maximilians-Universität München, Luisenstr. 37, 80333 Munich, Germany
*
Author to whom correspondence should be addressed.
Received: 9 September 2009 / Revised: 29 October 2009 / Accepted: 30 October 2009 / Published: 9 November 2009
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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.
Keywords: land use classification; supervised classification; nearest neighbors; agricultural land cover; crops land use classification; supervised classification; nearest neighbors; agricultural land cover; crops
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Samaniego, L.; Schulz, K. Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN) and Landsat Remote Sensing Imagery. Remote Sens. 2009, 1, 875-895.

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