Name
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. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | Hierarchical clustering, Gaussian test, fractional distance, TerraSAR-X |
Field | DocType | ISSN |
Hierarchical clustering,Computer vision,Canopy clustering algorithm,CURE data clustering algorithm,Minkowski distance,Pattern recognition,Correlation clustering,Computer science,Determining the number of clusters in a data set,Consensus clustering,Artificial intelligence,Cluster analysis | Conference | 2153-6996 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Yao | 1 | 0 | 1.01 |
| Otmar Loffeld | 2 | 386 | 58.35 |
| Mihai Datcu | 3 | 893 | 111.62 |