基于幅相一致性校正的穩(wěn)健植被參數(shù)反演方法
doi: 10.11999/JEIT140261
基金項目:
國家自然科學基金(61271024, 61201292, 61201283)，新世紀優(yōu)秀人才支持計劃(NCET-09-0630)，全國優(yōu)秀博士學位論文作者專項資金(FANEDD-201156)，國家部級基金，中國航天科技集團公司航天科技創(chuàng)新基金和中央高?；究蒲袠I(yè)務(wù)費專項資助課題
Forest Parameters Inversion Based on Nonstationarity Compensation and Mapping Space Regularization
-
摘要: 植被參數(shù)反演是極化干涉合成孔徑雷達(PolInSAR)的重要應(yīng)用。傳統(tǒng)反演方法未考慮觀測樣本數(shù)據(jù)幅度和相位的非平穩(wěn)特性，以及觀測信號非均勻分布對其散布區(qū)域線性變化主導方向估計的影響。針對這些問題，該文首先采用經(jīng)過幅度和相位一致性校正的數(shù)據(jù)樣本估計極化相干矩陣，提高了極化干涉復相干系數(shù)的估計性能，并提出了映射空間均衡化(MSR)處理技術(shù)以消除觀測信號非均勻分布對主導方向提取的影響，通過引入主成分分析(PCA)方法進一步提高了參數(shù)反演算法的性能。利用歐空局(ESA)發(fā)布的軟件PolSARPro仿真驗證了該文方法在植被參數(shù)反演方面具有更好的穩(wěn)健性和估計精度。
-
關(guān)鍵詞:
- 極化干涉合成孔徑雷達 /
- 植被參數(shù)反演 /
- 非平穩(wěn)校正 /
- 映射空間均衡化 /
- 主成分分析
Abstract: Forest parameters inversion is an important application of Polarimetric Interference Synthetic Aperture Radar (PolInSAR). The traditional inversion method does not take into account the amplitude and phase non-stationary of observation, and its non-uniform distribution effect on the estimation of the principal linear change direction. Aiming at these problems, an amplitude and phase calibration approach is proposed to compensate the polarization coherence matrix nonstationarity, to enhance the performance of complex coherences estimation. Moreover, this paper develops a Mapping Space Regularization (MSR) technology which promises to be able to eliminate the non-uniform distribution effect of sample coherences on the linear variation of complex coherences. Based on MSR, the Principal Component Analysis (PCA) is further introduced to the linear variation model extraction. Processing results of ESA PolSARpro simulated data verify the better robustness and estimation accuracy of the proposal in forest parameters inversion. -
計量
- 文章訪問數(shù): 1523
- HTML全文瀏覽量: 196
- PDF下載量: 469
- 被引次數(shù): 0
下載:
