A standardized radiometric normalization method for change detection using remotely sensed imagery

Joon Heo, Thomas W. FitzHugh

Research output: Contribution to journalReview article

74 Citations (Scopus)

Abstract

The image normalization process aims to remove radiometric differences between multitemporal images that are due to non-surface factors. Accurate normalization is essential for image processing procedures that use multi-date imagery, such as change detection. Linear regression using temporally invariant targets is a widely accepted method for normalization. How- ever, except for the criteria for selecting target points, there is no standard method for conducting this important procedure. This paper proposes a standardized radiometric normalization method for detecting and deleting outliers and obtaining the optimal linear equation for a given set of target points. The method consists of a linear regression model and a studentized residual method for outlier determination. Standardized decision criteria such as R2 and confidence range for t-test are discussed and investigated, as are the issues of band selection and normalization target size. Four variants of the method are tested here, using a pair of Landsat TM images 10 years apart and corresponding training sets and accuracy assessment data. As a result, a standardized computation procedure is proposed, which uses band-by-band linear regression, single pixel targets, and a very conservative 99 percent confidence interval for determining outliers.

Original languageEnglish
Pages (from-to)173-181
Number of pages9
JournalPhotogrammetric Engineering and Remote Sensing
Volume66
Issue number2
Publication statusPublished - 2000 Feb 1

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Linear regression
imagery
outlier
Linear equations
Image processing
Pixels
accuracy assessment
image processing
Landsat thematic mapper
confidence interval
detection
method
normalisation
pixel

All Science Journal Classification (ASJC) codes

  • Computers in Earth Sciences

Cite this

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abstract = "The image normalization process aims to remove radiometric differences between multitemporal images that are due to non-surface factors. Accurate normalization is essential for image processing procedures that use multi-date imagery, such as change detection. Linear regression using temporally invariant targets is a widely accepted method for normalization. How- ever, except for the criteria for selecting target points, there is no standard method for conducting this important procedure. This paper proposes a standardized radiometric normalization method for detecting and deleting outliers and obtaining the optimal linear equation for a given set of target points. The method consists of a linear regression model and a studentized residual method for outlier determination. Standardized decision criteria such as R2 and confidence range for t-test are discussed and investigated, as are the issues of band selection and normalization target size. Four variants of the method are tested here, using a pair of Landsat TM images 10 years apart and corresponding training sets and accuracy assessment data. As a result, a standardized computation procedure is proposed, which uses band-by-band linear regression, single pixel targets, and a very conservative 99 percent confidence interval for determining outliers.",
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A standardized radiometric normalization method for change detection using remotely sensed imagery. / Heo, Joon; FitzHugh, Thomas W.

In: Photogrammetric Engineering and Remote Sensing, Vol. 66, No. 2, 01.02.2000, p. 173-181.

Research output: Contribution to journalReview article

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