Abstract
As an essential parameter in synthetic aperture radar (SAR) images, the equivalent number of looks (ENL) not only indicates the speckle noise level in multi-look SAR data but also can be used for evaluating the region homogeneity level. Currently, time-series polarimetric (interferometric) SAR (TSPol(In)SAR) data are increasingly abundant, but traditional equivalent number of looks (ENL) estimators only use polarimetric information from a mono-temporal observation and do not consider the temporal characteristics or interferometric coherence of ground targets. Therefore, this paper puts forward four novel ENL estimators to overcome the restrictions of inadequate observation information. Firstly, based on the traditional trace moment estimator for polarimetric SAR data (TM-PolSAR), we extend it to both PolInSAR and TSPolInSAR data and then propose both TM-PolInSAR and TM-TSPolInSAR estimators, respectively. Secondly, for both TSPolSAR and single-reference TSPolInSAR data, we estimate the ENL by stacking the trace moments (STM) of multitemporal coherency matrices, called STM-TSPolSAR and STM-TSPolInSAR estimators, respectively. Therefore, these proposed ENL estimators can effectively deal with most of the requirements of TSPol(In)SAR data types in practical applications, mainly including statistical distribution modeling and region homogeneity evaluation. The simulation and real experiments detailedly compare the proposed four ENL estimators to the classical TM-PolSAR estimator and quantitatively analyze the estimation performance. The proposed estimators have obtained the ENL with less bias and standard deviation than the traditional estimator, especially in case of small spatial samples coherency matrices. Additionally, these STM-TSPolSAR, STM-TSPolInSAR, and TM-TSPolInSAR estimators have provided more effective statistical characteristics with the increase of the time-series size. It has been demonstrated that the proposed STM-TSPolSAR estimator considers the time-varying polarimetric characteristics of the crop and detects many edges that the traditional estimator cannot discover, which means a superior capability of region homogeneity evaluation.
Highlights
As an essential parameter in synthetic aperture radar (SAR) images, the equivalent number of looks (ENL) indicates the speckle noise level, and smaller ENL value means a higher level of speckle noise [1]
It is worth noticing that the proposed fouItrisEtNheLmeosstismuiatatoblrescahlosiocehfoarvPeomlInoSrAeRsduaptaertoiosrelpecotttehnetpiarol poofseddeTtaMil-PporleInsSeArvRaetsiotinm,awtorhich will be furthearmdoinsgcuthsesseedEiNnLSeescttiimoantsor5s.anHdow6e.ver, it is necessary to quantitatively analyze the effect of the series size on the estimation performance of the other three estimators, including stacking the trace moments (STM)-TSPolSAR, STM-PolInSAR, and trace moment (TM)-TSPolInSAR
To overcome the limitation of the inadequate observation information in traditional estimators, this paper is based on the TSPol(In)SAR data and proposes four novel ENL estimators, including TM-PolInSAR, STM-TSPolSAR, STM-TSPolInSAR, and TM-TSPolInSAR estimators
Summary
As an essential parameter in synthetic aperture radar (SAR) images, the equivalent number of looks (ENL) indicates the speckle noise level, and smaller ENL value means a higher level of speckle noise [1]. The equivalent number of looks (ENL) is proposed to describe the degree of averaging in the postprocessing procedure accurately It is necessary for many statistical-theory-based applications to accurately estimate the ENL value because it is an important input parameter in the statistical distribution modeling of multi-look SAR data, including sigma filter [2,3,4], change detection [5,6], and target classification [7,8,9,10,11]. For single polarimetric SAR data, a homogeneous area is usually selected manually from the original images before the ENL estimation, where the assumptions of fully developed speckle noise and ignorable texture assure that the observed images follow complex circular Gaussian distribution [1].
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