Object-Based Superresolution Land-Cover Mapping From Remotely Sensed Imagery

Yuehong Chen, Yong Ge*, Gerard B.M. Heuvelink, Ru An, Yu Chen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

27 Citations (Scopus)


Superresolution mapping (SRM) is a widely used technique to address the mixed pixel problem in pixel-based classification. Advanced object-based classification will face a similar mixed phenomenon--a mixed object that contains different land-cover classes. Currently, most SRM approaches focus on estimating the spatial location of classes within mixed pixels in pixel-based classification. Little if any consideration has been given to predicting where classes spatially distribute within mixed objects. This paper, therefore, proposes a new object-based SRM strategy (OSRM) to deal with mixed objects in object-based classification. First, it uses the deconvolution technique to estimate the semivariograms at target subpixel scale from the class proportions of irregular objects. Then, an area-to-point kriging method is applied to predict the soft class values of subpixels within each object according to the estimated semivariograms and the class proportions of objects. Finally, a linear optimization model at object level is built to determine the optimal class labels of subpixels within each object. Two synthetic images and a real remote sensing image were used to evaluate the performance of OSRM. The experimental results demonstrated that OSRM generated more land-cover details within mixed objects than did the traditional object-based hard classification and performed better than an existing pixel-based SRM method. Hence, OSRM provides a valuable solution to mixed objects in object-based classification.

Original languageEnglish
Pages (from-to)328-340
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number1
Early online date19 Sep 2017
Publication statusPublished - Jan 2018


  • Area-to-point kriging (ATPK)
  • deconvolution
  • Image segmentation
  • mixed object
  • Optimization
  • Remote sensing
  • remotely sensed imagery
  • Satellites
  • Spatial resolution
  • superresolution mapping (SRM).


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