Deep Clustering With Intraclass Distance Constraint for Hyperspectral Images

Xian Wei, Wei Yao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)


The high dimensionality of hyperspectral images often results in the degradation of clustering performance. Due to the powerful ability of potential feature extraction and nonlinear representation, deep clustering algorithms have become a hot topic in hyperspectral remote sensing. Different tasks often need different features. However, the current deep clustering algorithms generally separate feature extraction from clustering, which results in the extracted features that are not constrained by clustering tasks. Therefore, the features extracted by these algorithms may not be suitable for clustering. To address this issue, we adopt intraclass distance as a constraint condition and proposed an intraclass distance constrained deep clustering algorithm for hyperspectral images. The proposed algorithm propagates the clustering error back to the feature mapping process of the autoencoder network, so as to realize the constraint of clustering objective on feature extraction and make the extracted features more suitable for clustering tasks. In addition, the proposed algorithm simultaneously completes network optimization and clustering, which is more efficient. Experimental results demonstrate the intense competitiveness of the proposed algorithm in comparison with state-of-the-art clustering methods for hyperspectral images.

Original languageEnglish
Article number9206066
Pages (from-to)4135-4149
Number of pages15
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number5
Publication statusPublished - May 2021


  • Deep learning
  • hyperspectral images clustering
  • intraclass distance constraint
  • low-dimensional (LD) representation
  • remote sensing

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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