On The Physical Quantitative Assessment of Model-Based PolSAR Decompositions

arxiv(2020)

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摘要
The performance of model-based decomposition approaches rooted in the Freeman-Durden concept is an active research line in PolSAR field according to the considerable attention it has deserved along the last twenty years. Certainly, most of subsequent proposals have been driven by the only objective of getting a better qualitative balance among scattering mechanisms according to theoretical expectations. This idea is not a negative aspect per se, as has led to a more rigorous understanding of orientation effects in both urban and natural areas and hence to improved land cover classifications. However, an in-depth quantitative analysis on the output parameters is usually lacking in this topic. The attention has been mostly paid to the power of dominant contributions, whereas the accuracy and interpretation of other parameters useful for practical applications have been almost systematically overlooked. The questions that remain to be answered are: What is the actual role of all parameters describing the models? Can we assign them a consistent physical interpretation or are some of them acting just as fitting parameters? The present work aims to promote the discussion on these open issues regarding the quantitative assessment of model-based PolSAR decomposition schemes. To proceed with, we have simulated the coherency matrix according to one of existing general models and different scenarios. The inversion performance has been analysed in terms of the histograms of output parameters, standard deviation and bias. The analysis reveals that even the backscattering powers associated with all three basic scattering mechanisms are estimated with a non-negligible error higher than 10% for some cases. Despite these conclusions are subject to a particular model and inversion approach they suggest that a careful consideration of physically-based decompositions outcomes should be taken.
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关键词
decompositions,physical quantitative assessment,model-based
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