Performance assessment of different relative radiometric normalization approaches applied to land cover change detection

LI Mingshi, MEI Zhaorong

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2017, Vol. 41 ›› Issue (04) : 108-114.

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南京林业大学学报（自然科学版）
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JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2017, Vol. 41 ›› Issue (04) : 108-114. DOI: 10.3969/j.issn.1000-2006.201605063

Performance assessment of different relative radiometric normalization approaches applied to land cover change detection

  • LI Mingshi, MEI Zhaorong
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Abstract

【Objective】Seek a relatively simple, automated and objective normalization method with a high accuracy in land cover change detection. 【Method】Considering the Landsat OLI 2013(July)images as the reference, four relative radiometric normalization approaches including the pseudo-invariant features(PIF), the temporally invariant cluster(TIC), the wall-to-wall regression(WWR), and the weighted invariant pixels(WIP)were used to normalize the Landsat OLI 2015(September)image. Based on normalization, the change vector analysis(CVA)method was used to detect land cover change information, and these detected land cover changes were validated by visually interpreting high-resolution Google images covering the study area to calculate a spatial agreement index. 【Result】Results indicated that the spatial agreement measures for PIF, TIC, WWR and WIP methods were 79.63%,81.75%,72.72% and 82.59%, respectively, and the agreement measure would further escalate if the change type dependent thresholds were properly specified.【Conclusion】 With respect to objectivity, automation degree and change detection accuracy of the normalization methods, the TIC and WIP approaches are considered to be suitable for engineering oriented image normalization operations.

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LI Mingshi, MEI Zhaorong. Performance assessment of different relative radiometric normalization approaches applied to land cover change detection[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2017, 41(04): 108-114 https://doi.org/10.3969/j.issn.1000-2006.201605063

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