Sampling based approximate spectral clustering ensemble for unsupervised land cover identification

Approximate spectral clustering (ASC), a recently popular approach for unsupervised land cover identification, applies spectral clustering on a reduced set of data representatives (found by sampling or quantization). ASC enables extraction of clusters with different characteristics by utilizing various information types (such as distance, local density distribution and data topology) for accurate similarity definition. However, selection of a sampling / quantization method and a similarity criterion is of great importance for optimal clustering. Alternatively, we propose sampling based ASC ensemble (SASCE) to exploit different similarity criteria with selective sampling by merging their partitionings into a consensus result. We show the outperformance of the proposed ensemble SASCE on four land cover datasets in comparison with their individual clusterings.