Research Papers

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

[+] Author Affiliations
Nisha Bao

China University of Geosciences, School of Land Science &Technology, Beijing 100083, China

The University of Queensland, Centre for Mined Land Rehabilitation, Brisbane, Queensland 4072, Australia

Alex M. Lechner, Andrew Fletcher, David Mulligan

The University of Queensland, Centre for Mined Land Rehabilitation, Brisbane, Queensland 4072, Australia

Andrew Mellor

RMIT University, Victoria, 2476, Australia

Zhongke Bai

China University of Geosciences, School of Land Science &Technology, Beijing 100083, China

J. Appl. Remote Sens. 6(1), 063578 (Sep 24, 2012). doi:10.1117/1.JRS.6.063578
History: Received February 1, 2012; Revised June 20, 2012; Accepted August 6, 2012
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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.

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© 2012 Society of Photo-Optical Instrumentation Engineers

Citation

Nisha Bao ; Alex M. Lechner ; Andrew Fletcher ; Andrew Mellor ; David Mulligan, et al.
"Comparison of relative radiometric normalization methods using pseudo-invariant features for change detection studies in rural and urban landscapes", J. Appl. Remote Sens. 6(1), 063578 (Sep 24, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.063578


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