Sunflower biomass estimation using a scattering model and a neural network algorithm

被引：29
|
作者：
Del Frate, F
Wang, LF
机构：
[1] Univ Roma Tor Vergata, Fac Ingn, Dipartimento Informat Sistemi & Prod, I-00133 Rome, Italy
[2] MIT, Elect Res Lab, Cambridge, MA 02139 USA
来源：
INTERNATIONAL JOURNAL OF REMOTE SENSING | 2001年 / 22卷 / 07期
关键词：
D O I：
10.1080/01431160151144323
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
Inversion of biomass for sunflower fields using radar backscattering data has been carried out with neural network algorithms. An electromagnetic model is used to generate the scattering coefficients for training and testing of the net. The model is validated with experimental data obtained from the Montespertoli test site during the Remote Sensing Campaign Mac-Europe 91. The inversion results show that the neural network is capable of performing the retrieval with good accuracy. By optimizing the structural complexity of the net, a better inversion result is obtained.
引用
收藏
页码：1235 / 1244
页数：10
相关论文
共 50 条
  • [21] k Satisfiability Programming by using Estimation of Distribution Algorithm in Hopfield Neural Network
    Rasli, Norul Fazira Ahmad
    Kasihmuddin, Mohd Shareduwan Mohd
    Mansor, Mohd Asyraf
    Basir, Md Faisal Md
    Sathasivam, Saratha
    PROCEEDINGS OF THE 27TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM27), 2020, 2266
  • [22] An Evolving Neural Network Using an Ant Colony Algorithm for a Permeability Estimation of the Reservoir
    Irani, R.
    Nasimi, R.
    PETROLEUM SCIENCE AND TECHNOLOGY, 2012, 30 (04) : 375 - 384
  • [23] Manifold absolute pressure estimation using neural network with hybrid training algorithm
    Muslim, Mohd Taufiq
    Selamat, Hazlina
    Alimin, Ahmad Jais
    Haniff, Mohamad Fadzli
    PLOS ONE, 2017, 12 (11):
  • [24] Estimation of groundwater level using a hybrid genetic algorithm-neural network
    Hosseini, Z.
    Nakhaei, M.
    POLLUTION, 2015, 1 (01): : 9 - 21
  • [25] Using a neural network learning algorithm suitable for the best estimation of nonlinear system
    Wang, XL
    Jin, ZS
    Shen, GX
    Tang, DL
    PROCEEDINGS OF THE 4TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-4, 2002, : 2030 - 2034
  • [26] Neural Network Estimation of LAL/VPC Resions of Silkmoth using Genetic Algorithm
    Chiba, Ryosuke
    Hashimoto, Sunao
    Kazawa, Tomoki
    Kanzaki, Ryohei
    Ota, Jun
    IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, : 2395 - 2400
  • [27] Function estimation using a neural-fuzzy network and an improved genetic algorithm
    Lam, H.K. (hkl@eie.polyu.edu.hk), 1600, Elsevier Inc. (36):
  • [28] Function estimation using a neural-fuzzy network and an improved genetic algorithm
    Lam, HK
    Ling, SH
    Leung, FHF
    Tam, PKS
    INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2004, 36 (03) : 243 - 260
  • [29] MULTI OBJECTIVE MACHINING ESTIMATION MODEL USING ORTHOGONAL AND NEURAL NETWORK
    Yusoff, Yusliza
    Zain, Azlan Mohd
    Sharif, Safian
    Sallehuddin, Roselina
    JURNAL TEKNOLOGI, 2016, 78 (12-2): : 11 - 18
  • [30] Construction equipment productivity estimation using artificial neural network model
    Ok, Seung C.
    Sinha, Sunil K.
    CONSTRUCTION MANAGEMENT AND ECONOMICS, 2006, 24 (10) : 1029 - 1044
12345