An Approximate Spectral Clustering Ensemble for High Spatial Resolution Remote-Sensing Images
Özet
Unsupervised clustering of high spatial resolution
remote-sensing images plays a significant role in detailed landcover identification, especially for agricultural and environmental
monitoring. A recently promising method is approximate spectral clustering (SC) which enables spectral partitioning for large
datasets to extract clusters with distinct characteristics without a
parametric model. It also facilitates the use of various information types via advanced similarity criteria. However, it requires
an empirical selection of a similarity criterion optimal for the
corresponding application. To address this challenge, we propose
an approximate SC ensemble (ASCE2) which fuses partitionings obtained by different similarity representations. Contrary to
existing spectral ensembles for remote-sensing applications, the
proposed ASCE2 employs neural gas quantization instead of random sampling, advanced similarity criteria instead of traditional
distance-based Gaussian kernel with different decay parameters,
and a two-level ensemble. We evaluate the proposed ASCE2 with
three measures (accuracy, adjusted Rand index, and normalized mutual information) using five remote-sensing images, two
of which are commonly available. We apply the ASCE2 in two
applications for agricultural monitoring: 1) land-cover identification to determine orchard fields using a WorldView-2 image
(0.5-m spatial resolution) and 2) finding lands in good agricultural condition using multitemporal RapidEye images (5-m spatial
resolution). Experimental results indicate a significant betterment
of the resulting partitionings obtained by the proposed ensemble,
with respect to the evaluation measures in these applications.
