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基于地相位優(yōu)化估計(jì)的RVoG三階段森林冠層高度反演
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國(guó)家自然科學(xué)基金項(xiàng)目(42061072),、云南省科技廳重大科技專項(xiàng)(202002AA100007-015)和云南省教育廳科學(xué)研究基金項(xiàng)目(2022Y579)


Forest Canopy Height Inversion in RVoG Three-stage Based on Optimal Estimation of Ground Phase
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    摘要:

    極化干涉合成孔徑雷達(dá)(PolInSAR)估測(cè)森林結(jié)構(gòu)參數(shù)中,,數(shù)據(jù)受基線長(zhǎng)度、信噪比,、環(huán)境地形以及雷達(dá)波長(zhǎng)的影響,,尤其在復(fù)雜森林環(huán)境條件下,會(huì)導(dǎo)致觀測(cè)到的復(fù)相干存在誤差,,從而影響最終的反演結(jié)果,。為解決此問(wèn)題,,首先探討了體相干選擇對(duì)RVoG三階段森林冠層高度反演的影響,以地相位為參考逐像素選擇距離地相位最遠(yuǎn)的相干性作為體相干,。其次改進(jìn)了地相位估計(jì)方法,,采用戴明回歸(DMR)和正交回歸(OGR)2種相干直線擬合方法來(lái)改進(jìn)地相位的估計(jì),并在DMR擬合方法中設(shè)置了不同的誤差比(0.3和0.6)來(lái)比較地相位估計(jì)方法對(duì)RVoG三階段森林冠層高度反演的影響,。研究結(jié)果表明:以地相位為參考逐像素選擇體相干的反演結(jié)果相較于直接使用HV極化通道的復(fù)相干γHV為體相干的反演精度有明顯提升,,決定系數(shù)(R2)由0.349增加到0.383,均方誤差由7.097m2降低到5.755m2,。在體相干優(yōu)化選擇的基礎(chǔ)上,,采用了戴明回歸和正交回歸對(duì)地相位估計(jì)方法進(jìn)行了改進(jìn)。表明基于最小二乘回歸(LSR)地相位估計(jì)的RVoG三階段反演精度最低,,采用DMR和OGR進(jìn)行相干線擬合的反演精度相較于LSR均有一定提升,,所有反演結(jié)果的決定系數(shù)(R2)均在0.440左右,均方誤差(MSE)均降低了2m2左右,。研究結(jié)果說(shuō)明采用RVoG三階段方法反演森林冠層高度時(shí),,在復(fù)相干存在誤差的情況下,用傳統(tǒng)最小二乘回歸(LSR)估計(jì)地相位進(jìn)行高度反演會(huì)對(duì)結(jié)果帶來(lái)一定誤差,,通過(guò)其他相干直線擬合方法來(lái)克服復(fù)相干誤差的影響能改善最終的森林冠層高度反演結(jié)果,,以地相位為參考選擇體相干的反演方法也更為合理。

    Abstract:

    In polarimetric interferometric synthetic aperture ray (PolInSAR) forest structure parameter estimation, the data are affected by the baseline length size, signal-to-noise ratio, environmental topography, and radar wavelength, especially under complex forest environmental conditions, which can lead to errors in the observed complex coherence and thus affect the final inversion results. Firstly the effect of volume coherence selection on the RVoG three-stage forest canopy height inversion was explored, and the coherence farthest from the ground phase was selected as the volume coherence with the ground phase as the reference pixel by pixel. Secondly, the ground phase estimation method was improved by using two coherence linear fitting methods, Deming regression (DMR) and orthogonal regression (OGR), to improve the estimation of the ground phase, and different error ratios (0.3 and 0.6) were set in the DMR fitting method to compare the effects of the ground phase estimation method on the RVoG three-stage forest canopy height inversion. The results showed that the inversion accuracy of the inversion of volume coherence with ground phase as the reference pixel-by-pixel selection was improved compared with that of the complex coherence with the HV polarization channel directly. The coefficient of determination(R2)was increased from 0.349 to 0.383, and the mean square error(MSE) was decreased from 7.097m2 to 5.755m2. Based on the optimal selection of the volume coherence, the ground phase estimation method was improved by using Deming regression and orthogonal regression. It was shown that the least squares regression (LSR)-based ground phase estimation had the lowest accuracy of RVoG three-stage inversion, using DMR and OGR for coherence line fitting had a certain improvement in inversion accuracy compared with LSR, and the coefficient of determination (R2) of all inversion results was around 0.440, and all MSE was reduced by about 2m2. The conclusions indicated that the forest canopy height inversion using the RVoG three-phase method introduced some errors in the height inversion by using the traditional LSR estimation of the ground phase in the presence of errors in the complex coherence. Using other coherence linear fitting methods to overcome the influence of the complex coherence error can improve the final forest canopy height inversion results, and it was also more reasonable to choose volume coherence inversion method with the ground phase as the reference.

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羅洪斌,朱泊東,岳彩榮,楊文俊,龍飛,徐婉婷.基于地相位優(yōu)化估計(jì)的RVoG三階段森林冠層高度反演[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2022,53(7):301-307. LUO Hongbin, ZHU Bodong, YUE Cairong, YANG Wenjun, LONG Fei, XU Wanting. Forest Canopy Height Inversion in RVoG Three-stage Based on Optimal Estimation of Ground Phase[J]. Transactions of the Chinese Society for Agricultural Machinery,2022,53(7):301-307.

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  • 收稿日期:2021-12-08
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  • 在線發(fā)布日期: 2022-07-10
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