Knn density-based clustering for high dimensional multispectral images

Abstract

High resolution and high dimension satellite images cause problems for clustering methods due to clusters of different sizes, shapes and densities. The most common clustering methods, e.g. K-means and ISODATA, do not work well for such kinds of datasets. In this work, density estimation techniques and density-based clustering methods are exploited. Density-based clustering is well known in data mining to classify a data set based on its density parameters, where lower density areas separate high-density areas, although it can only work with a simple data set in which cluster densities are not very different. Out contribution is to propose the k nearest neighbor (knn) density-based rule for high dimensional dataset and to develop a new knn density-based clustering (KNNCLUST) for such complex dataset. KNNCLUST is stable, clear and easy to understand and implement. The number of clusters is automatically determined. These properties are illustrated by the segmentation of a multispectral image of a floodplain in the Netherlands.