Estimation of subpixel target size for remotely sensed imagery

Chein I. Chang, Hsuan Ren, Chein Chi Chang, Francis D'Amico, James O. Jensen

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


One of the challenges in remote sensing image processing is subpixel detection where the target size is smaller than the ground sampling distance, therefore, embedded in a single pixel. Under such a circumstance, these targets can be only detected spectrally at the subpixel level, not spatially as ordinarily conducted by classical image processing techniques. This paper investigates a more challenging issue than subpixel detection, which is the estimation of target size at the subpixel level. More specifically, when a subpixel target is detected, we would like to know "what is the size of this particular target within the pixel?" The proposed approach is to estimate the abundance fraction of a subpixel target present in a pixel, then find what portion it contributes to the pixel that can be used to determine the size of the subpixel target by multiplying the ground sampling distance. In order to make our idea work, the subpixel target abundance fraction must be accurately estimated to truly reflect the portion of a subpixel target occupied within a pixel. So, a fully constrained linear unmixing method is required to reliably estimate the abundance fractions of a subpixel target for its size estimation. In this paper, a recently developed fully constrained least squares linear unmixing is used for this purpose. Experiments are conducted to demonstrate the utility of the proposed method in comparison with an unconstrained linear unmixing method, unconstrained least squares method, two partially constrained least square linear unmixing methods, sum-to-one constrained least squares, and nonnegativity constrained least squares.

Original languageEnglish
Pages (from-to)1309-1320
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number6
StatePublished - Jun 2004


  • Fully constrained least squares (FCLS)
  • Fully constrained least squares linear unmixing (FCLSLU)
  • Nonnegativity constrained least squares (NCLS)
  • Sum-to-one constrained least squares (SCLS)
  • Unconstrained least squares (ULS)


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