Comparison of relative radiometric normalization methods using pseudo-invariant features for change detection studies in rural and urban landscapes

Bao, Nisha, Lechner, Alex M., Fletcher, Andrew, Mellor, Andrew, Mulligan, David and Bai, Zhongke (2012) Comparison of relative radiometric normalization methods using pseudo-invariant features for change detection studies in rural and urban landscapes. Journal of Applied Remote Sensing, 6 1: . doi:10.1117/1.JRS.6.063578

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Author Bao, Nisha
Lechner, Alex M.
Fletcher, Andrew
Mellor, Andrew
Mulligan, David
Bai, Zhongke
Title Comparison of relative radiometric normalization methods using pseudo-invariant features for change detection studies in rural and urban landscapes
Journal name Journal of Applied Remote Sensing   Check publisher's open access policy
ISSN 1931-3195
Publication date 2012-09-24
Sub-type Article (original research)
DOI 10.1117/1.JRS.6.063578
Open Access Status File (Publisher version)
Volume 6
Issue 1
Total pages 18
Place of publication Bellingham, WA, United States
Publisher S P I E - International Society for Optical Engineering
Language eng
Formatted abstract
Relative radiometric normalization (RRN) to remove sensor effects, solar and atmospheric variation from at-sensor radiance values is often necessary for effective detection of temporal change. Traditionally, pseudo-invariant features (PIFs) are chosen subjectively, where as an analyst manually chooses known objects, often man-made, that should not change over time. An alternative method of selecting PIFs uses a principal component analysis (PCA) to select the PIFs. We compare the two RRN methods using PIFs in multiple Landsat images of urban and rural areas in Australia. An assessment of RRN quality was conducted including measurements
of slope, root mean square error, and normalized difference vegetation index. We found that in urban areas both methods performed similarly well. However, in the rural area the automated PIF selection method using a PCA performed better due to the rarity of built features that are required for the manual PIF selection. We also found that differences in performance of the manual and automated methods were dependent on the accuracy assessment method tested. We conclude with a discussion on the relative merits of different RRN methods and practical advice
on how to apply the automated PIF selection method.
Keyword Relative radiometric normalization
Pseudo-invariant features
Change detection
Arid-zone
Landsat thematic mapper
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes In press; Paper 12024 received Feb. 1, 2012; revised manuscript received Jun. 20, 2012; accepted for publication Aug. 6, 2012; published online Sep. 24, 2012.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Centre for Mined Land Rehabilitation Publications
Official 2013 Collection
 
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Created: Thu, 27 Sep 2012, 19:32:56 EST by Dr Alex Lechner on behalf of Centre For Mined Land Rehabilitation