An Approximate Spectral Clustering Ensemble for High Spatial Resolution Remote-Sensing Images

Kadim Tasdemir*, Yaser Moazzen, Isa Yildirim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Unsupervised clustering of high spatial resolution remote-sensing images plays a significant role in detailed land-cover 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.

Original languageEnglish
Article number7103289
Pages (from-to)1996-2004
Number of pages9
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number5
Publication statusPublished - 1 May 2015

Bibliographical note

Publisher Copyright:
© 2015 IEEE.


  • Approximate spectral clustering (SC)
  • cluster ensemble
  • clustering
  • geodesic similarity
  • land-cover identification


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