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

Samaniego, Luis and Schulz, Karsten (December 2009): Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN) and Landsat Remote Sensing Imagery. In: Remote Sensing, Vol. 1, No. 4: pp. 875-895

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.

• Item Type: Journal article
• Form of publication: Publisher's Version
• Keywords: land use classification; supervised classification; nearest neighbors; agricultural land cover; crops
• Faculties: Geosciences > Department of Geography > Physical Geography and Remote Sensing
• Subjects: 500 Science > 550 Earth sciences and geology
• ISSN: 2072-4292
• Place of Publication: POSTFACH, CH-4005 BASEL, SWITZERLAND
• Language: English