A hierarchical Patch Clustering Method for High Resolution TerraSAR-X Images

Abstract

In this paper, we present a Gaussian test-based hierarchical clustering method for high-resolution TerraSAR-X images. The purpose is to obtain homogeneous clusters. k-means is used to split image features to create a hierarchical structure. As image feature vectors usually fall into high dimensional feature space, we test different distance metrics, in order to try to tackle the curse of dimensionality problem. With prepared datasets, we evaluate the clustering results by defining a homogeneity percentage. The results show that by using Gabor texture feature, the Gaussian test-based hierarchical patch clustering method is able to obtain homogeneous clusters. Meanwhile, fractional distance or Minkowski distance performs better than Euclidean or Manhatten distance.