Random Similarity-Based EntropyAlpha Classification of PolSAR Data

NSSC OpenIR > 微波遥感部

	Random Similarity-Based EntropyAlpha Classification of PolSAR Data
	Li, Dong; Zhang, Yunhua
作者部门	微波遥感部
发表期刊	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
	2017
ISSN	1939-1404
语种	英语
摘要	A fast and competent alternative to the widely used CloudePottier entropyalpha ($H/ \alpha $) classification is developed for the rapid response application of polarimetric synthetic aperture radar (PolSAR) data. Random similarity which measures both the scattering similarity and randomness of polarimetric scatterers is used to enable an $H/ \alpha $ -like classification in terms of two key parameters, i.e., the similarity-based angle $\alpha _{s}$ and entropy $H_{s}$, as the alternatives to the CloudePottier angle $\alpha $ and entropy H, respectively. Parameters $\alpha _{s}$ and $H_{s}$ maintain the same physical information as parameters $\alpha $ and H, so the existing knowledge regarding $\alpha $ and H can be naturally extended to them. Angle $\alpha _{s}$ measures scattering mechanism and is ranged within the same interval 0, 90 as $\alpha $ while entropy $H_{s}$ measures scattering randomness which is also a logarithm within the interval 0, 1 similar to H. The pixelwise eigendecomposition in the calculation of $\alpha $ and H is avoided for $\alpha _{s}$ and $H_{s}$, and the resulted efficiency improvement is, thus, considerable. By rigorously modeling the $\alpha _{s}\hbox{--}\alpha $ and the $H_{s}\hbox{--}H$ relationship to illustrate the competence of the $H_{s}\hbox{--}\alpha _{s}$ combination in discrimination of target and to identify the searching ranges for the boundary determination, an $H_{s}/ \alpha _{s}$ classification is then devised with the boundaries of the eight effective classes being determined by an optimization to minimize the misclassification and further integrated on different PolSAR images to remove the possible bias from dataset for general applicability. Comparative experiment on both space-borne and airborne PolSAR datasets with $H/ \alpha $ indicates that $H_{s}/ \alpha _{s}$ can achieve very consistent roll-invariant target discrimination as $H/ \alpha $ (overall accuracy 95, kappa coefficient 0.95) but with averagely 150 times higher efficiency although the LAPACK-based eigenanalysis tool has been used to accelerate the eigendecomposition for $H/ \alpha $. Preliminary result from the adaptive model-based classification reveals that the $H_{s}$-involved boundaries in $H_{s}/ \alpha _{s}$ are independent of a particular PolSAR dataset. IEEE
收录类别	EI
文献类型	期刊论文
条目标识符	http://ir.nssc.ac.cn/handle/122/6130
专题	微波遥感部
推荐引用方式 GB/T 7714	Li, Dong,Zhang, Yunhua. Random Similarity-Based EntropyAlpha Classification of PolSAR Data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2017.
APA	Li, Dong,&Zhang, Yunhua.(2017).Random Similarity-Based EntropyAlpha Classification of PolSAR Data.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
MLA	Li, Dong,et al."Random Similarity-Based EntropyAlpha Classification of PolSAR Data".IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2017).

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