Next Article in Journal
Regional Assessment of Aspen Change and Spatial Variability on Decadal Time Scales
Previous Article in Journal
Hyperspectral Reflectance and Fluorescence Imaging to Detect Scab Induced Stress in Apple Leaves
Remote Sens. 2009, 1(4), 875-895; doi:10.3390/rs1040875
Article

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

1
 and 2,*
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
Download PDF [1570 KB, uploaded 19 June 2014]

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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Export to BibTeX |
EndNote


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.

View more citation formats

Article Metrics

Comments

Citing Articles

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert