Semisupervised hyperspectral classification using task-driven dictionary learning with laplacian regularization

Zhangyang Wang, Nasser M. Nasrabadi, Thomas S. Huang

Research output: Contribution to journalArticlepeer-review


We present a semisupervised method for single-pixel classification of hyperspectral images. The proposed method is designed to address the special problematic characteristics of hyperspectral images, namely, high dimensionality of hyperspectral pixels, lack of labeled samples, and spatial variability of spectral signatures. To alleviate these problems, the proposed method features the following components. First, being a semisupervised approach, it exploits the wealth of unlabeled samples in the image by evaluating the confidence probability of the predicted labels, for each unlabeled sample. Second, we propose to jointly optimize the classifier parameters and the dictionary atoms by a task-driven formulation, to ensure that the learned features (sparse codes) are optimal for the trained classifier. Finally, it incorporates spatial information through adding a Laplacian smoothness regularization to the output of the classifier, rather than the sparse codes, making the spatial constraint more flexible. The proposed method is compared with a few comparable methods for classification of several popular data sets, and it produces significantly better classification results.

Original languageEnglish (US)
Article number6868294
Pages (from-to)1161-1173
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number3
StatePublished - Mar 1 2015
Externally publishedYes


  • Bilevel optimization
  • Hyperspectral image classification
  • Semisupervised learning
  • Sparse coding
  • Spatial Laplacian regularization
  • Task-driven dictionary learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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