Spectrally-consistent relative radiometric normalization for multi-temporal Landsat 8 images

Muhammad Aldila Syariz, Chao Hung Lin, Bo Yi Lin

Research output: Contribution to conferencePaperpeer-review


Radiometric normalization is a necessary pre-processing step since the acquired satellite images contain uncertainties such as atmospheric effect and surface reflectance. For most historical experiments, the associated atmospheric properties may be difficult to obtain even for planned acquisitions. Relative normalization is an alternative method whenever absolute reflectance properties are not required. The key to relative normalization is the selection of pseudo-invariant features (PIFs) in an image. PIFs of a bi-temporal image is a group of pixels which are statistically nearly-constant over the period of the bi-temporal image acquisitions. Several methods, such as manual selection, histogram matching, and principal component analysis, had been proposed for PIFs extraction. Yet, a change in pixel’s spectral signature before and after normalization, called spectral inconsistency, is detected whenever those PIFs extraction methods, associated with a regression process, are performed. To overcome this shortcoming, the commonly used PIFs selection, called multivariate alteration detection (MAD), is utilized as it considers the relationship among bands. Further, a constrained regression is adopted to enforce the normalized pixel’s spectral signature to be consistent as possible. This approach is applied to multi-temporal Landsat-8 imageries. Moreover, spectral distance and similarities are utilized for evaluating the consistency of the normalized pixel’s spectral signature.

Original languageEnglish
Publication statusPublished - 2017
Externally publishedYes
Event38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017 - New Delhi, India
Duration: 23 Oct 201727 Oct 2017


Conference38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017
CityNew Delhi


  • Constrained regression
  • Multivariate alteration detection
  • Pseudo-invariant features (PIFs)
  • Relative normalization
  • Spectral consistency


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