基于改進(jìn)的BP神經(jīng)網(wǎng)絡(luò)裸露地表土壤水分反演模型對比
本文關(guān)鍵詞:基于遺傳BP神經(jīng)網(wǎng)絡(luò)的主被動遙感協(xié)同反演土壤水分,由筆耕文化傳播整理發(fā)布。
[1] 舒寧.微波遙感原理[M].武漢:武漢大學(xué)出版社,2003. Shu N.Microwave Remote Sensing Principle[M].Wuhan:Wuhan University Press,2003.[2] 李森.基于IEM的多波段、多極化SAR土壤水分反演算法研究[D].北京:中國農(nóng)業(yè)科學(xué)院,2007. Li S.Soil Moisture Inversion Model Research of Multi-Band and Multi-Polarization SAR Based on IEM[D].Beijing:Chinese Academy of Agrieultural Sciences,2007.[3] Oh Y,Sarabandi K,Ulaby F T.Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(6):1348-1355.[4] Shi J C,Wang J,Hsu A Y,et al.Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data[J].IEEE Transactions on Geoseience and Remote Sensing,1997,35(5):1254-1266.[5] 田芳明,周志勝,黃操軍,等.BP神經(jīng)網(wǎng)絡(luò)在土壤水分預(yù)測中的應(yīng)用[J].電子測試,2009(10):14-17. Tian M F,Zhou Z S,Huang C J,et al.Application of BP artificial neural network on prediction of soil water content[J].Electronic Test,2009(10):14-17.[6] 黃飛.基于AMSR-E和BP神經(jīng)網(wǎng)絡(luò)的川中丘陵區(qū)土壤水分反演[D].四川農(nóng)業(yè)大學(xué),2012:1-76. Huang F.Soil Moisture Retrieval Using AMSR-E Data by BP Neural Network for Sichuan Middle Hilly Area[D].Sichuan Agricultural University,2012:1-76.[7] 余凡,趙英時(shí),李海濤.基于遺傳BP神經(jīng)網(wǎng)絡(luò)的主被動遙感協(xié)同反演土壤水分[J].紅外與毫米波學(xué)報(bào),2012,31(3):283-288. Yu F,Zhao Y S,Li H T.Soil moisture retrieval based on GA-BP neural networks algorithm[J].Journal of Infrared and Millimeter Waves,2012,31(3):283-288.[8] 林潔,陳效民,張勇,等.基于BP神經(jīng)網(wǎng)絡(luò)的太湖典型農(nóng)田土壤水分動態(tài)模擬[J].南京農(nóng)業(yè)大學(xué)學(xué)報(bào),2012,35(4):140-144. Lin J,Chen X M,Zhang Y,et al.Simulation of soil moisture dynamics based on the BP neural network in the typical farmland of Tai Lake region[J].Journal of Nanjing Agricultural University,2012,35(4):140-144.[9] 蔡滿軍,程曉燕,喬剛.一種改進(jìn)BP網(wǎng)絡(luò)學(xué)習(xí)算法[J].計(jì)算機(jī)仿真,2009,26(7):172-174. Cai M J,Cheng X Y,Qiao G.An improved learning algorithm for BP network[J].The Computer Simulation,2009,26(7):172-174.[10] 陳思.BP神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)率參數(shù)改進(jìn)方法[J].長春師范學(xué)院學(xué)報(bào):自然科學(xué)版,2010,29(1):26-28. Chen S.Learning rate parameter improve methods for BP neutral network[J].Journal of Changchun Normal University:Natural Science,2010,29(1):26-28.[11] 高紅.BP神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)率的優(yōu)化方法[J].長春師范學(xué)院學(xué)報(bào):自然科學(xué)版,2010,29(2):29-31. Gao H.Optimal methods of learning rate for BP neutral network[J].Journal of Changchun Normal University:Natural Science,2010,29(2):29-31.[12] 李翱翔,陳健.BP神經(jīng)網(wǎng)絡(luò)參數(shù)改進(jìn)方法綜述[J].電子科技,2007(2):79-82. Li A X,Chen J.Summarize of parameter improve methods for BP neural network[J].Electronic Science and Technology,2007(2):79-82.[13] Hecht-Nielson R.Theory of the backpropagation neural network[C]//Proceedings of the International Joint Conference on Neural Networks.Washington,DC,USA:IEEE,1989:593-605.[14] Fung A K,Li Z,Chen K S.Backscattering from a randomly rough dielectric surface[J].IEEE Transactions on Geoscience and Remote Sensing,1992,30(2):356-369.[15] Pan H,Wang X Y,Chen Q,et al.Application of BP neural network based on genetic algorithm[J].Computer Application,2005,25(12):2777-2779.[16] Barre H M J,Duesmann B,Kerr Y H.SMOS:The mission and the system[J].IEEE Transactions on Geoscience and Remote Sensing,2008,46(3):587-593.[17] 趙英時(shí).遙感應(yīng)用分析原理與方法[M].北京:科學(xué)出版社,2003:136-154. Zhao Y S.Analysis Principle and Method of Remote Sensing Applications[M].Beijing:Science Press,2003:136-154.[18] Kerr Y H,Waldteufel P,Wigneron J P,et al.The SMOS mission:New tool for monitoring key elements of the global water cycle[J].Proceedings of the IEEE,2010,98(5):666-687.[19] Kerr Y H,Waldteufel P,Wigneron J P,et al.Soil moisture retrieval from space:The Soil Moisture and Ocean Salinity(SMOS) mission[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(8):1729-1735.[20] 張玲,蔣金豹,崔希民,等.利用ANFIS方法反演裸土區(qū)土壤水分含量[J].國土資源遙感,2013,25(2):63-68.doi:10.6046/gtzyyg.2013.02.12. Zhang L,Jiang J B,Cui X M,et al.ANFIS method to soil moisture inversion in bare region[J].Remote Sensing for Land and Resources,2013,25(2):63-68.doi:10.6046/gtzyyg.2013.02.12.[21] 余凡,趙英時(shí).ASAR和TM數(shù)據(jù)協(xié)同反演植被覆蓋地表土壤水分的新方法[J].中國科學(xué):地球科學(xué),2011,41(4):532-540. Yu F,Zhao Y S.A new semi-empirical model for soil moisture content retrieval by ASAR and TM data in vegetation-covered areas[J].Science China Earth Sciences,2011,54(12):1955-1964.[22] Bacour C,Baret F,Béal D,et al.Neural network estimation of LAI,fAPAR,fCover,and LAI×Cab,from top of canopy MERIS reflectance data:Principles and validation[J].Remote Sensing of Environment,2006,105(4):313-325.[23] 高婷婷.基于IEM的裸露隨機(jī)地表土壤水分反演研究[D].烏魯木齊:新疆大學(xué),2010. Gao T T.Study on Soil Moisture Inversion of Bare Random Surface based on IEM Model[D].Urumqi:Xinjiang University,2010.[24] Merzouki A,Bannari A,Teillet P M,et al.Statistical properties of soil moisture images derived from Radarsat-1 SAR data[J].International Journal of Remote Sensing,2011,32(19):5443-5460.[25] 李芹.青藏高原地區(qū)主被動微波遙感聯(lián)合反演土壤水分的研究[D].北京:首都師范大學(xué),2011. Li Q.Soil Moisture Inversion Research of Qinghai-Tibet Plateau by Passive and Aetive Microwave Remote Sensing[D].Beijing:The Capital Normal University,2011.
本文關(guān)鍵詞:基于遺傳BP神經(jīng)網(wǎng)絡(luò)的主被動遙感協(xié)同反演土壤水分,由筆耕文化傳播整理發(fā)布。
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