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一種面向高異質(zhì)性極化SAR圖像的等效視數(shù)非監(jiān)督估計方法

胡丁晟,  仇曉蘭,  StianN.Anfinsen,  雷斌

胡丁晟, 仇曉蘭, StianN.Anfinsen, 雷斌. 一種面向高異質(zhì)性極化SAR圖像的等效視數(shù)非監(jiān)督估計方法[J]. 電子與信息學(xué)報, 2017, 39(10): 2287-2293. doi: 10.11999/JEIT170014
引用本文: 胡丁晟, 仇曉蘭, StianN.Anfinsen, 雷斌. 一種面向高異質(zhì)性極化SAR圖像的等效視數(shù)非監(jiān)督估計方法[J]. 電子與信息學(xué)報, 2017, 39(10): 2287-2293. doi: 10.11999/JEIT170014
HU Dingsheng, QIU Xiaolan, Stian N. Anfinsen, LEI Bin. Unsupervised Estimation of the Equivalent Number of Looks in PolSAR Image with High Heterogeneity[J]. Journal of Electronics & Information Technology, 2017, 39(10): 2287-2293. doi: 10.11999/JEIT170014
Citation: HU Dingsheng, QIU Xiaolan, Stian N. Anfinsen, LEI Bin. Unsupervised Estimation of the Equivalent Number of Looks in PolSAR Image with High Heterogeneity[J]. Journal of Electronics & Information Technology, 2017, 39(10): 2287-2293. doi: 10.11999/JEIT170014

一種面向高異質(zhì)性極化SAR圖像的等效視數(shù)非監(jiān)督估計方法

doi: 10.11999/JEIT170014
基金項目: 

國家自然科學(xué)基金(61331017), 高分三號共性關(guān)鍵技術(shù)(30-Y20A12-9004-15/16, 03-Y20A11-9001-15/16)

Unsupervised Estimation of the Equivalent Number of Looks in PolSAR Image with High Heterogeneity

Funds: 

The National Natural Science Foundation of China (61331017), The GF-3 High-Resolution Earth Observation System (30-Y20A12-9004-15/16, 03-Y20A11-9001-15/16)

  • 摘要: 等效視數(shù)(ENL)是極化SAR多視數(shù)據(jù)統(tǒng)計模型的重要參數(shù)。而一些極化SAR圖像的自動化應(yīng)用中，需要在沒有人工干預(yù)下實現(xiàn)ENL非監(jiān)督估計?，F(xiàn)有的等效視數(shù)非監(jiān)督估計方法在異質(zhì)程度較高的圖像中就難以得到準(zhǔn)確估計結(jié)果。針對這一問題，該文提出一種將混合區(qū)域剔除與紋理信息聚類相結(jié)合的等效視數(shù)非監(jiān)督估計方法，有效地減弱了地物混合及紋理兩類主要異質(zhì)因素對估計結(jié)果的影響。通過仿真數(shù)據(jù)和不同復(fù)雜度的實際圖像驗證了該方法的有效性。
    Abstract: Equivalent Number of Looks (ENL) is an important parameter in statistical modelling of multi-look Polarimetric SAR (PolSAR) data. In some automated applications of PolSAR images, it is necessary to estimate the ENL in an unsupervised way without any manual intervention. The existing unsupervised estimation of ENL can not obtain accurate estimates for the images with high heterogeneity. To address this issue, a novel unsupervised estimation method is proposed here. It combines the mixture elimination and clustering based on texture, which reduces the effect of two main heterogeneity factors, mixture and texture. The validity of this method is evaluated with simulated and real data of different complexity.
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出版歷程
  • 收稿日期:  2017-01-03
  • 修回日期:  2017-03-21
  • 刊出日期:  2017-10-19

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