A Novel Image Classification Algorithm Using Overcomplete Wavelet Transforms

Soe W. Myint, Tong Zhu, Baojuan Zheng

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


A novel frequency-based classification framework and new wavelet algorithm (Wave-CLASS) is proposed using an overcomplete decomposition procedure. This approach omits the downsampling procedure and produces four-texture information with the same dimension of the original image or window at infinite scale. Three image subsets of QuickBird data (i.e., park, commercial, and rural) over a central region in the city of Phoenix were used to examine the effectiveness of the new wavelet overcomplete algorithm in comparison with a widely used classical approach (i.e., maximum likelihood). While the maximum-likelihood classifier produced < 78.29% overall accuracies for all three image subsets, the Wave-CLASS algorithm achieved high overall accuracies - 95.05% for the commercial subset (Kappa = 0.94), 93.71% for the park subset (Kappa = 0.93), and 89.33% for the rural subset (Kappa = 0.86). Results from this study demonstrate that the proposed method is effective in identifying detailed urban land cover types in high spatial resolution data.

Original languageEnglish (US)
Article number7047799
Pages (from-to)1232-1236
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number6
StatePublished - Jun 2015


  • Classification
  • high spatial resolution
  • infinite scale
  • overcomplete decomposition
  • urban land cover
  • wavelet transforms

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering


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